Vokasional (TVET). Menurut World Economic Forum (WEF) Future of Jobs Report 2023 kemahiran seperti pemikiran kritikal, kreativiti, dan penyelesaian masalah kompleks adalah antara yang paling dikehendaki oleh industri menjelang tahun 2025. Kajian oleh Guaman-Quintanilla et al. (2018) juga menekankan bahawa pendekatan design thinking dapat memperkukuh kemahiran ini dalam kalangan pelajar [7]. B. Pendekatan Design Thinking dalam PendidikanDesign thinking merupakan pendekatan berpusatkanmanusia yang menekankan empati, kreativiti dan penyelesaian masalah secara iteratif. Pendekatan ini telah digunakan secara meluas dalam pendidikan untuk meningkatkan kemahiran pemikiran kritikal dan inovasi pelajar. Kajian oleh Le Chi Nguy n! et! al.! (2025)! menunjukkan! bahawa! integrasi! design thinking dalam pendidikan STEM dapat meningkatkan kemahiran penyelesaian masalah dan kreativiti pelajar [8]. Selain itu, kajian oleh Fischer et al. (2023) dan Shiet Ling dan Ruhizan (2022) mendapati bahawa penggunaan design thinking dalam kalangan pelajar universiti dapat memperkukuh pemikiran kreatif dan keupayaan menyelesaikan masalah yang kompleks [9] [10]. Rajah 1 berikut merupakan slaid penyampaian yang digunakan dalam memberikan pemahaman pelajar terhadap kaedah design thinking. RAJAH 1. SLAID PENYAMPAIAN KONSEP DESIGN THINKINGC. Peranan Generative AI dalam PendidikanGenerative AI, seperti ChatGPT telah menunjukkan potensibesar dalam menyokong proses pembelajaran dan penyelesaian masalah. Penggunaan Generative AI dapat mempercepatkan inovasi dan meningkatkan kreativiti dalam pendidikan dengan menyokong penjanaan idea prototaip dan penyelesaian masalah [11], [12], [13], [14]. Namun, penggunaan Generative AI juga menimbulkan cabaran, seperti risiko kebergantungan berlebihan yang boleh mengurangkan pemikiran kritikal pelajar [15]. Oleh itu, penting untuk mengintegrasikan Generative AI dalam kurikulum bagi memastikan penggunaan AI yang beretika dan berkesan. Rajah 2 memaparkan penyampaian terhadap pelajar bagaimana menulis penyataan masalah dengan menggunakan ChatGPT sebagai persona dan prompting yang digunakan. RAJAH 2. BAGAIMANA MENULIS PERNYATAAN MASALAH MELALUI PERANAN GENERATIVE AID. Integrasi ChatGPT dalam Proses Design ThinkingPenggunaan ChatGPT dalam proses design thinking telah menunjukkan hasil yang positif dalam meningkatkan pemahaman pelajar terhadap masalah dan dalam menjana idea penyelesaian yang kreatif. Kajian oleh Makers Empire (2023) menunjukkan bahawa ChatGPT dapat menyokong setiap fasa dalam proses design thinking, termasuk fasa empati, definisi masalah, ideasi, prototaip dan ujian. Selain itu, kajian oleh Fischer et al. (2023) mendapati bahawa penggunaan ChatGPT RAJAH 3. CONTOH BAGAIMANA MENULIS PERNYATAAN MASALAH MELALUI PERANAN GENERATIVE AI DIPEROLEHIdalam bengkel design thinking membantu pelajar dalam menganalisis masalah dan menjana idea, walaupun terdapat batasan dalam memahami konteks empati secara mendalam. Rajah 3 menunjukkan contoh setelah ChatGPT digunakan bagi setiap peringkat pernyataan masalah.III. METODOLOGI KAJIANKajian dibangunkan dengan menggunakan reka bentuk tinjauan kualitatif, melalui kajian tinjauan kualitatif membolehkan .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7242
penyelidik melihat keberkesanan penggunaan generative AI khususnya ChatGPT dalam meningkatkan kemahiran penyelesaian masalah kompleks dengan menggunakan pendekatan design thinking. Temuduga berkumpulan dilaksanakan bagi mendapatkan pelbagai perspektif dan pandangan daripada peserta mengenai penggunaan ChatGPT dalam meningkatkan kemahiran penyelesaian masalah kompleks. Kajian ini melibatkan 35 orang pelajar Kolej Komuniti yang sedang mengikuti Kursus Projek yang dilaksanakan secara work based learning (WBL). Peserta dibahagikan kepada tujuh Focus Group Discussion(FGD). IV. DAPATAN KAJIANBerdasarkan data tematik daripada tujuh FGD yang menilai keberkesanan ChatGPT dalam meningkatkan kemahiran penyelesaian masalah kompleks menggunakan pendekatan design thinking, berikut adalah analisis tematik yang dapat disusun berdasarkan tema dan hasil petikan temu bual. JADUAL 1. TEMA DAN PERINCIAN TEMU BUALImplikasi dapatan kajian menunjukkan bahawa Generative AIkhususnya ChatGPT berperanan sokongan interaktif yang efektif dalam mempermudah pemahaman konsep-konsep abstrak melalui pendekatan design thinking iaitu fasa empathize dan ideate dalam menyediakan maklum balas dinamik serta penjelasan dalam pelbagai bentuk seperti teks, visual serta contoh yang memudahkan pemahaman. Selain itu, ChatGPT secara signifikan berupaya mengurangkan beban kognitif pengguna melalui penyediaan contoh praktikal dan langkah sistematik yang tersusun, menjadikannya sangat berkesan dalam konteks mempercepatkan adaptasi metodologi pendekatan design thinking dalam penyelesaian masalah praktikal di industri. ChatGPT juga bertindak sebagai simulator yang menghubungkan jurang antara teori akademik dan aplikasi praktikal dunia sebenar dengan mensimulasikan senario seperti melalui prototaip pantas dan analisis kes, seterusnya membantu pelajar mengaplikasikan pengetahuan secara berkesan. Di samping itu juga ChatGPT juga berupaya memacu proses kreatif melalui pendekatan design thinking, ChatGPT menyokong aspek teknikal seperti penjanaan dokumen (user persona, laporan) dan penulisan berstruktur (scripting, storyboard) yang berupaya menjimatkan masa dan meningkatkan kualiti output, sekaligus memperkukuh keberkesanan pembelajaran dan pembangunan kemahiran penyelesaian masalah kompleks. V. DAPATAN KAJIANDalam hal ini, penggunaan model AI seperti ChatGPT dilihat berpotensi besar dalam membantu pelajar TVET menginterpretasi situasi masalah dengan lebih mendalam, serta merangsang idea penyelesaian yang lebih pelbagai dan praktikal [16]. Selain itu juga ChatGPT digunakan sebagai alat sokongan dalam setiap fasa ini untuk membantu pelajar dalam memahami konteks masalah dan mencadangkan penyelesaian yang lebih inovatif [17]. Berdasarkan dapatan yang diperoleh keberkesanan penggunaan Generative AI, khususnya ChatGPT, dalam meningkatkan kemahiran penyelesaian masalah kompleks dapat dilihat melalui peranannya sebagai panduan interaktif yang memudahkan pemahaman konsep abstrak dalam pendekatan design thinking. Melalui perlaksanaan contoh praktikal dan langkah sistematik yang telah dilaksanakan, ChatGPT dilihat membantu mengurangkan beban kognitif pelajar, sekaligus memperkukuh proses pembelajaran yang berpusatkan pelajar di institusi seperti institusi TVET. Selain itu, fungsi ChatGPT sebagai simulator penghubung jurang antara teori akademik dan aplikasi praktikal dalam dunia sebenar, membolehkan pelajar mengaplikasikan konsep secara lebih berkesan. Dapatan kajian menunjukkan bahawa penggunaan ChatGPT bukan sahaja menyokong proses kreatif design thinking, malah turut meningkatkan aspek teknikal seperti penulisan, yang seterusnya menjimatkan masa dan meningkatkan kualiti hasil kerja pelajar dalam menyelesaikan masalah kompleks. Secara keseluruhan, kajian ini memberikan gambaran yang jelas tentang bagaimana penggunaan Generative AI dapat memperkasa kemahiran penyelesaian masalah pelajar TVET melalui pendekatan design thinking, responsif dengan keperluan industri semasa dan meningkatkan kebolehpasaran graduan. .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7243
RUJUKAN[1] E. Karan and L.!Brown,!ìEnhancing!Studentsí!Problem-solving Skills through Project-based!Learning,î!Journal of Problem Based Learning in Higher Education, vol. 10, May 2022, doi: 10.54337/ojs.jpblhe.v10i1.6887. [2] H. Hassani, AI and education: The integration of artificialintelligence in education systems. Springer, 2022.[3] A.!Zary!and!N.!Zary,!ìArtificial!Intelligence!in!Technical!andVocational Education and Training: Empirical Evidence,Implementation Challenges, a nd Future Directions,î!Preprints.org, 2025.[4] T. Brown, Change by design: How design thinking creates new alternatives for business and society. HarperBusiness, 2021.[5] R.!Razzouk!and!V.!Shute,!ìWhat!Is!Design!Thinking!and!Why!Is!ItImportant?,î!Rev Educ Res, vol. 82, no. 3, 2012, doi:10.3102/0034654312457429.[6] R.!Singh!and!A.!Sharma,!ìDesign!thinking!and!generative!AI:!A!new!paradigm!for!enhancing!student!creativity,î!Educ Res Rev, vol. 38, p. 100478, 2023.[7] C. Guaman-Quintanilla!and!others,!ìDesign!Thinking!in!Education:Reviewing!the!Past!for!Setting!Future!Research,î!Int J Technol Des Educ, 2018.[8] L.!C.!Nguy n!and!others,!ìIntegrating!Design!Thinking!into!STEM!Education: Enhancing Problem-Solving Skills of High SchoolStudents,î!European Journal of Mathematics and Science Education, 2025.[9] D.!Lapp,!T.!D.!Wolsey,!D.!Fisher,!and!N.!Frey,!ìGraphic!novels\":What!elementary!teachers!think!about!their!instructional!value,î!Journal of Education, vol. 192, no. 1, pp. 23ñ35, 2012.[10] S.!L.!Lai!and!M.!Y.!Ruhizan,!ìPersepsi!Guru!Luar!Bandar!TerhadapPenerapan!Design!Thinking!Dalam!Pendidikan!STEM,î!JurnalDunia Pendidikan, 2022, doi: 10.55057/jdpd.2022.4.1.39. [11] S.!Kim!and!J.!Park,!ìEmpathy!and!innovation:!The!role!of!designthinking in technology-enhanced!learning!environments,î!ComputEduc, vol. 192, p. 104659, 2023.[12] V.!Bilgram!and!F.!Laarmann,!ìAccelerating!Innovation!WithGenerative AI: AI-Augmented Digital Prototyping and InnovationMethods,î!IEEE Engineering Management Review, vol. 51, no. 2, 2023, doi: 10.1109/EMR.2023.3272799.[13] Y.!Zhang,!R.!Liang,!and!H.!Ma,!ìTeaching!innovation!in!ComputerNetwork!Course!for!Undergraduate!Students!with!Packet!Tracer,î!IERI Procedia, vol. 2, p. 504, 2012, [Online]. Available:http://library.oum.edu.my/oumlib/ezproxylogin?url=http://search.ebscohost.com/login.aspx?direct=true&db=edo&AN=84561024[14] Y.!Dai,!A.!Liu,!and!C.!P.!Lim,!ìReconceptualizing!ChatGPT!andgenerative AI as a student-driven!innovation!in!higher!education,î!inProcedia CIRP, 2023. doi: 10.1016/j.procir.2023.05.002. [15] U.!Wilkens,!ìKey!Takeaways!from!the!AI!in!TVET!Expert!Meetingin!Magdeburg,î!TVET Journal, 2025.[16] C.!Tangmanee!and!T.!Rotworaphor,!ìAn!Exploration!into!ThaiInternet!Users!í!Perception!towards!Onscreen!English!Fonts!withImplication!to!Electronic!Commerce,î!Journal of InternationalManagement Studies (1993-1034), vol. 8, no. 1, pp. 20ñ32, 2013. [17] H. Hassani, AI and education: The integration of artificialintelligence in education systems. Springer, 2022..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7244
BijakKimpal: Web-Based Learning for Welding Technology CourseAimi Ruzaini AhmadUniversiti Teknologi [email protected] Farzeeha AliUniversiti Teknologi MalaysiaJohor [email protected]²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ffl &DUERQ6WHHO3ODWHPRGXOH7KHGHYHORSPHQWSURFHVV IROORZHG WKH$'',( LQVWUXFWLRQDO GHVLJQ PRGHO HQFRPSDVVLQJ ILYHSKDVHVffl DQDO\\VLV GHVLJQ GHYHORSPHQWLPSOHPHQWDWLRQ DQGHYDOXDWLRQ'DWDZHUHFROOHFWHGWKURXJKSUHWHVWDQGSRVWWHVWWR DVVHVV WKH HIIHFWLYHQHVV RI WKH ZHEEDVHG OHDUQLQJDSSURDFK 7KH ILQGLQJV LQGLFDWHG WKDW %LMDN.LPSDO KDG DSRVLWLYH LPSDFW RQ VWXGHQWV¶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eywordsóWeb-based learning, Welding, ADDIE ModelI. INTRODUCTIONThe Fourth Industrial Revolution (IR 4.0) has changed the way education is delivered, emphasizing digital skills, personalized learning, and lifelong learning. In response, Malaysia has introduced key policies such as the Dasar Pendidikan Digital and Dasar TVET Negara, which support the integration of digital technologies in teaching and learning, especially in the Technical and Vocational Education and Training (TVET) sector. These policies aim to prepare graduates with both technical and digital skills needed for modern industries. However, many TVET institutions in Malaysia still depend on traditional teaching methods, particularly in courses such as Welding Technology. These methods typically involve face-to-face instruction supported by printed materials, which limit studentsí opportunities to revisit and deeply understand theoretical content. Face-to-face teaching in TVET is generally more practical than online approaches (Razak et al., 2022). Nevertheless, the absence of interactive and flexible digital learning tools poses a challenge for students to meet current industry demands, which increasingly require digital competencies and self-directed learning skills.To address this issue, this study developed and evaluated BijakKimpal, a web-based learning platform designed for the Shielded Metal Arc Welding (SMAW) Process: Carbon Steel Plate 3 module. This module was chosen because it is one of the most difficult for students to master, as shown by assessment results and instructor feedback. The platform was built using the ADDIE instructional design model and includes multimedia content, quizzes, and self-paced learning activities. Its goal is to improve the teaching and learning process by providing students with flexible and engaging ways to study. This study also supports the national TVET Madani initiative, which promotes inclusive, innovative, and future-ready technical education.II. LITERATURE REVIEWWeb-based learning has become a key driver in modern education, offering greater accessibility, interactivity, and personalization. In the context of TVET, it plays a crucial role in bridging the gap between theoretical knowledge and practical skills. The use of multimedia and interactive content further enhances accessibility, student engagement, and the development of hands-on competencies (Kinsman et al., 2021; Incesu et al., 2024). This study is based on two key learning theories. First, the Constructivist Learning Theory (Hein, 1991) encourages students to build knowledge through experience and interaction. Second, the Cognitive Theory of Multimedia Learning (Mayer, 2002) highlights how combining visuals and audio helps learners understand better by reducing mental overload.Although digital learning is growing in many fields, there is still limited research on its use in welding education within TVET. This study fills that gap by examining how a web-based platform can improve learning outcomes in a welding module. National policies such as the Dasar Pendidikan Digital and Dasar TVET Negara emphasize the need to integrate digital tools in education and train educators in ICT. Supporting this direction, the TVET Madani agenda promotes inclusive, equitable, and futureready technical education for all learners (ISEAS, 2024)..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7245
III. METHODOLOGYA. Research DesignThis study used a quasi-experimental design with twogroups of Semester 2 students from an Industrial Training Institute (ILP) in Johor, Malaysia, enrolled in the Welding Technology course. The control group (N=27) received traditional face-to-face instruction, while the treatment group (N=28) used the BijakKimpal web-based learning platform. Purposive sampling was used to select Semester 2 students because the SMAW Process: Carbon Steel Plate 3 module is taught during this semester. This ensured that all participants were learning the same targeted content.Students completed a pre-test before the lesson to assess their initial knowledge and a post-test after the lesson to measure improvement. The results were analyzed using paired samples t-tests in SPSS to compare scores before and after learning. This analysis helped determine how effective each teaching method was in improving student performance.B. Data AnalysisThe pre-test and post-test data were analyzed usingSPSS. The main method used was the Paired Samples ttest, which compares the average scores before and after the intervention within the same group. For the control group, the test measured the effectiveness of traditional face-to-face learning. For the treatment group, it evaluated the impact of the BijakKimpal web-based learning platform. These tests helped determine if the changes in student scores were statistically significant, and whether BijakKimpal was more effective than traditional teaching in improving learning outcomes for the welding module.C. Instructional Design ModelThe development of BijakKimpal was guided by theADDIE model, which provides a structured framework for instructional design. The model's five phases: Analysis, Design, Development, Implementation, and Evaluation were systematically applied to ensure the creation of an effective and learner-centered web-based platform.Figure 1: ADDIE Model1) Analysis PhaseIn this phase, the researcher identified the learningneeds and difficulties faced by students in the Welding Technology course. A preliminary discussion with instructors showed that the SMAW Process: Carbon Steel Plate 3 module was one of the most challenging, mainly due to its theoretical nature and the lack of visual, repeatable learning materials. To address this, a curriculum and learner needs analysis was conducted. The target group was Semester 2 students, as this module is taught during that semester. The researcher also defined the expected learning outcomes and used a pre-test to measure studentsí prior knowledge, followed by a post-test to evaluate their progress. Both tests were designed based on a test specification table provided by the Department of Manpower.The researcher also reviewed the Written Instructional Materials (WIM) to identify key topics, including theory (e.g., metal properties, steel content, weldability) and practice (e.g., welding in 3G and 4G positions). To guide the web platform development, a checklist of important features was created and validated by experts. This checklist helped ensure the platform was easy to use, engaging, and effective. A development timeline was also prepared to organize the process and manage time efficiently.2) Design PhaseIn the design phase, the instructional structure,navigation, and layout of the BijakKimpal platform were planned. The learning content was divided into two sections such as theory and introduction of practical. Each containing videos, notes, topic-based quizzes, and final assessments. The design focused on easy navigation and clear learning goals to ensure an interactive and flexible learning experience. To guide the development process, a flow chart was created to map out the platformís structure and features. This flow chart helped simplify the work during the development phase. Figure 2 shows the flow chart for the development of the BijakKimpal web-based learning platform.The flowchart for the BijakKimpal web-based learning platform is shown the main sections include Home, Module, ILJTM, and Contact Us. Users begin at the main page, where they either register or log in to access the system. After logging in, students can start the learning process, which is divided into two parts: Theory Learning and Introduction of Practical Learning. Each section includes topics with video and notes content, followed by quizzes. Students may repeat quizzes as needed before moving to the next topic. After completing all topics, students take a final test to assess their understanding. Instructors can also view student results through the system, with access to all student scores under the same institute. This structured flow ensures a step-by-step, selfpaced learning experience for users..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7246
Figure 2: FlowchartIn addition, user interface mock-ups were prepared to visualize the platformís learning flow, including login, module selection, and access to learning materials. These mock-ups helped ensure the platform was intuitive and user-friendly. Figures 3 to 9 display the user interface mock-ups designed for the platform.Figure 3: Mock-ups for main pageFigure 4: Mock-ups for login pageFigure 5: Mock-ups for learning module pageFigure 6: Mock-ups for learning pageFigure 7: Mock-ups for quiz pageFigure 8: Mock-ups for quiz mark page.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7247
Figure 9: Mock-ups for results page3) Development PhaseIn this phase, the design was transformed into a fullyfunctional web-based learning platform using JavaScript and the Cursor code editor. The learning content was enriched with multimedia elements, including videos created with Microsoft PowerPoint and Canva, and voice narration generated using TTSFree AI. The development process was carried out using a Lenovo IdeaPad Gaming 3 laptop, while video recordings were captured using a Samsung Z Flip 4 smartphone. These tools supported the creation of a platform that is interactive, user-friendly, and engaging for learners. Figures 10 to 16 illustrate the output of the web-based learning platform development.Figure 10: Main pageThe main page of the BijakKimpal platform presents the core focus of the learning module, which is the SMAW Process: Carbon Steel Plate 3. A prominently displayed ìStart Learningî button enables users to begin the learning process immediately. The top navigation menu provides access to key sections, including Home, Module, ILJTM, and Contact Us. To access the learning content securely, both students and instructors are required to log in using their registered credentials.Figure 11: Login pageThe login page of the BijakKimpal web-based learning platform provides a secure access point for registered users, including both students and instructors. Users are required to enter a Username and Password to access the platformís learning materials and features. This page ensures that only authorized individuals can enter the system, thereby maintaining the integrity and privacy of user data.Figure 12: Learning module pageThe Learning Module page presents structured content for the SMAW Process: Carbon Steel Plate 3 course. It is divided into two main components: Theory Learning and Introduction to Practical Learning. The theory section includes three topics covering key concepts such as metal properties, steel composition, and weldability. Meanwhile, the practical section introduces two topics focusing on welding techniques in the 3G and 4G positions.Figure 13: Learning page.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7248
The learning page on the BijakKimpal platform provides organized content for each topic. Students are required to watch a learning video before taking the quiz. To support understanding, students can also download notes and slides provided on the page. These helps reinforce what is shown in the video.Figure 14: Quiz pageThe quiz page in the BijakKimpal platform allows students to assess their understanding of each topic after completing the learning content. Each question provides immediate feedback, indicating whether the selected answer is correct or incorrect. This formative assessment approach helps reinforce learning by allowing students to identify misconceptions and correct them in real time. The system displays the number of questions answered and allows users to proceed to the next question using the ìNext Questionî button. This feature supports active learning and ensures students are engaged throughout the module.Figure 15: Quiz mark pageThe quiz result page provides immediate feedback on studentsí performance after completing a topic-based quiz. This automated assessment feature allows learners to monitor their understanding and identify areas for improvement. A ìRetake Quizî button is also provided, enabling students to repeat the quiz if needed. Such immediate feedback mechanisms are crucial in web-based learning environments as they promote active participation and self-directed learning.Figure 16: Result pageThe Result Page on the BijakKimpal platform provides a summary of studentsí performance in both quizzes and final tests. For each topic, it displays the date, percentage score, number of correct answers, and total number of questions, enabling students to monitor their learning progress. The page is divided into two main sections: Individual Results and Institution Summary. The Individual Results section presents each studentís own quiz and test performance by topic, while the Institution Summary section allows instructors to view the overall quiz results for all students within the same institution.4) Implementation PhaseDuring the implementation phase, students weregranted access to the platform and guided through its use in a real classroom setting. The system was tested for usability and effectiveness, ensuring that students could navigate and engage with the content independently.5) Evaluation PhaseThe evaluation phase represented the final and mostessential stage of the development process. The efficiency of the BijakKimpal platform was evaluated through Alpha Testing, which involved feedback from 10 experts (5 subject-matter experts in Welding Technology and 5 experts in web technology). These experts were selected using purposive sampling based on their qualifications and professional experience.The evaluation focused on two main components such as technical functionality and instructional content. The technical assessment covered aspects such as user interface design, system performance, data privacy, and crossplatform compatibility. Most items received high scores, indicating that the platform performed well and met user expectations, although minor improvements were suggested for device responsiveness. The content evaluation examined the clarity, accuracy, instructional effectiveness, and media quality of the learning materials. The results revealed strong consensus among the experts regarding the overall quality of the content, with multimedia features effectively supporting student comprehension and engagement..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7249
IV. RESULTTo assess the effectiveness of the BijakKimpal webbased learning platform, pre-test and post-tests were administered to both the control group (conventional instruction) and treatment group (web-based learning). The results were statistically analysed using paired samples ttests. Additionally, summative feedback was gathered via questionnaires to complement quantitative findings.Students in the control group (N=27) followed a traditional face-to-face learning method. As shown in Table 1, the average pre-test score was 49.85 (SD=15.25), increasing to 64.89 (SD=17.71) after instruction, a mean improvement of 15.04 points. The paired t-test confirmed that the improvement was statistically significant (t(26)=-3.916, p<.001), indicating that conventional teaching contributed positively to studentsí understanding. However, the moderate gain and wide standard deviation suggest inconsistent individual performance, signalling a need for more adaptable and student-centred approaches.Table 1: Pre-test and Post-test Results ñ Control Group7HVW 0HDQ 6' 0HDQ'LIIHUHQFH W GI SYDOXHPre 49.85 15.25 -15.04 -3.916 26 < .001Post 64.89 17.71The treatment group (N=28) used the BijakKimpal web-based platform. The mean pre-test score was 50.04 (SD=17.53), which increased substantially to 85.77 (SD=9.00), resulting in a 35.73-point gain as shown in Table 2. The paired samples t-test indicated a highly significant difference (t(27)=-10.634, p<.001), confirming that the web-based learning platform had a strong impact on studentsí performance. The smaller standard deviation in post-test scores suggests improved consistency and understanding among students.Table 2: Pre-Test and Post-Test Results ñ Treatment Group7HVW 0HDQ 6' 0HDQ'LIIHUHQFHW GI SYDOXHPre 50.04 17.53 -35.73 -10.634 27 < .001Post 85.77 9.00V. DISCUSSIONThe finding shows that both conventional and webbased teaching methods significantly improved student performance. However, students who used the BijakKimpal platform showed much greater improvement in post-test scores compared to those who followed traditional instruction. This suggests that web-based learning is more effective in helping students understand and retain welding concepts, especially when the content is complex and technical. The success of BijakKimpal highlights the benefits of digital learning environments, which offer flexibility and allow students to learn at their own pace. This helps learners review difficult topics as often as needed, supporting better understanding and learning outcomes. The platform's multimedia features, interactive modules, and built-in quizzes also make the learning experience more engaging and student-centred.These findings align with the goals of the Fourth Industrial Revolution (IR 4.0) and Malaysiaís TVET Madani initiative, which call for more inclusive and technology-driven education. BijakKimpal supports this vision by offering quality learning tools that are accessible to students regardless of their location. The study also supports national policies like the Dasar Pendidikan Digital and Dasar TVET Negara, which promote digital integration and innovation in vocational education. Overall, BijakKimpal not only improves student achievement but also contributes to the broader goal of preparing a skilled, digital-ready workforce for the future.VI. CONCLUSIONThis study developed and evaluated BijakKimpal, a web-based learning platform for the SMAW Process: Carbon Steel Plate 3 module in TVET education. Guided by the ADDIE model and relevant learning theories, the platform aimed to offer a more flexible and engaging alternative to traditional instruction. Findings showed that while both teaching methods improved learning, BijakKimpal led to significantly greater and more consistent gains. Its multimedia content, quizzes, and selfpaced structure effectively supported student understanding and independent learning. Aligned with the Dasar Pendidikan Digital, Dasar TVET Negara, and TVET Madani, BijakKimpal demonstrates the potential of digital platforms to enhance TVET delivery and prepare students for IR 4.0. Future research should explore its use across other modules and its long-term impact on student outcomes.REFERENCES[1] Razak, A. N. A., Noordin, M. K., & Khanan, M. F. A. (2022). Digital learning in technical and vocational education and training (TVET) in public university, Malaysia. Journal of Technical Education andTraining, 14(3), 49-59. https://doi.org/10.30880/jtet.2022.14.03.005[2] Hein, G. E. (1991). Constructivist Learning Theory. Paper presented at the CECA (International Committee of Museum Educators) Conference, Jerusalem Israel, 15-22 October 1991, 1-10.[3] Mayer, R. E., & Moreno, R. (2002). Aids to computer-based multimedia learning. Learning and instruction, 12(1), 107-119.https://doi.org/10.1016/S0959-4752(01)00018-4[4] Incesu, O., Kara, ÷., & !enyuva, E. (2024). The effect of web based and traditional self breast examination education on nursing students' knowledge, skills and self-directed learning skills: A randomisedcontrolled study.. Nurse education in practice, 81, 104167. https://doi.org/10.1016/j.nepr.2024.104167[5] Kinsman, L., Cooper, S., Champion, R., Kim, J., Boyle, J., Cameron, A., Cant, R., Chung, C., Connell, C., Evans, L., McInnes, D., McKay, A., Norman, L., Penz, E., Rana, M., & Rotter, T. (2021). The impactof web-based and face-to-face simulation education programs onnurses' response to patient deterioration: A multi-site interrupted timeseries study.. Nurse education today, 102, 104939. https://doi.org/10.1016/j.nedt.2021.104939[6] ISEAS. (2024). TVET Madani in Malaysia: Charting a New Coursefor Inclusive Skills Development. ISEAS Perspective. https://www.iseas.edu.sg/wpcontent/uploads/2024/11/ISEAS_Perspective_2025_5.pdf.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7250
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Penilaian Kerosakan Terma Pada Komposit Hibrid Kenaf/Kevlar Menggunakan Pengimejan Ujian Tanpa Musnah (NDT) Asyraf Arif Bin Abu Bakar, Siti Madiha Muhammad Amir, Sapizah Rahim, Mohd Zaki Umar, Mohd Yusniyam Bin YusofKumpulan NDT Termaju, Bahagian Teknologi Industri, Agensi Nuklear Malaysia [email protected] Komposit hibrid yang menggabungkan gentian semula jadi dan sintetik berpotensi digunakan dalam industry kerana sifat mesra alam dan kekuatan mekanikalnya. Namun, pendedahan kepada suhu tinggi boleh menyebabkan kerosakan terma yang sukar dikesan secara visual, mengancam integriti struktur. Kajian ini bertujuan menilai kerosakan terma pada komposit hibrid kenaf/Kevlar menggunakan teknik pengimejan NDT seperti termografi inframerah (pasif/aktif), radiografi digital (2D/volumetrik) dan laser shearografi. Sampel komposit disediakan melalui proses hand lay-up dengan susunan lapisan dan bilangan ply yang berbeza. Kerosakan terma diinduksi menggunakan sumber panas (200°C) selama 5 minit. Analisis masa nyata (real time) menggunakan termografi inframerah pasif menunjukkan taburan suhu tidak sekata, manakala analisis pascakerosakan dengan radiografi digital dan laser shearografi mendapati cabaran dalam pengesanan kecacatan akibat ketumpatan gentian yang serupa. Hasil kajian mencadangkan perlunya penambahbaikan dalam penyediaan sampel dan pemilihan teknik NDT yang lebih sensitif. Penemuan ini menyumbang kepada pembangunan prosedur NDT khusus untuk komposit hibrid, menyokong industri dalam penilaian keselamatan bahan. .DWD NXQFL: Komposit hibrid, kerosakan terma, ujian tanpamusnah (NDT), termografi inframerah, radiografi digital. PENGENALANPenggunaan komposit kini menjadi sebahagian penting dalam industri pembuatan, antara bahan utama dalam membuat komposit adalah gentian karbon, gentian kaca dan pelbagai. Secara umumnya bahan tersebut dikenali sebagai gentian sintetik. Namun demikian, terdapat usaha menambah baik pembuatan komposit dengan menggunakan bahan baru iaitu gentian semula jadi seperti serat Kenaf dan Nenas. Umumnya, gentian semula jadi boleh menyumbang kepada banyak manfaat alam sekitar berbanding gentian sintetik. Sebagai contoh, penghasilan gentian semula jadi memberi kesan alam sekitar yang lebih rendah. Walaupun gentian semula jadi mempunyai potensi untuk menggantikan gentian sintetik, ia juga mempunyai beberapa kekangan yang diketahui umum, termasuk ciri mekanikal yang lemah berbanding gentian sintetik(1). Berdasarkan kekurangan tersebut, fabrikasi komposit hibrid diperkenalkan, yang terdiri daripada dua atau lebih jenis gentian dalam satu matriks bahan. Komposit hibrid akan memainkan peranan penting dalam pembuatan struktur kritikal berprestasi tinggi dalam industri komersial(2). Aplikasi ini perlu memastikan keselamatan dan kebolehpercayaan bahan-bahan tersebut. Dalam industri aeroangkasa dan automotif terutamanya, terdapat kebimbangan terhadap kecenderungan bahan komposit tertentu untuk mengalami kerosakan tidak dapat dipulihkan apabila terdedah kepada suhu tinggi. Apabila terdedah kepada suhu yang cukup tinggi sehingga menyebabkan degradasi resin dan menjejaskan kekuatan mekanikal pada suhu bilik secara signifikan(3). Di bawah ambang pendedahan tertentu, komposit ini mungkin kelihatan tidak rosak secara visual atau mikroskopik tetapi sebenarnya telah kehilangan sebahagian besar kekuatan asalnya. Selain itu, kerapuhan permukaan dan keretakan juga menyebabkan kehilangan kekuatan hentaman bahan tersebut. Oleh itu, struktur komposit hibrid yang terdedah kepada keadaan pemanasan berlebihan boleh mengalami kerosakan yang serius dan tidak dapat dipulihkan. Dalam kajian ini, Kenaf/Kevlar akan digunakan sebagai komposit hibrid untuk mengkaji kerosakan terma menggunakan modaliti pengimejan NDT seperti termografi inframerah, radiografi digital dan laser shearografi.METODOLOGIMetodologi kajian ini merangkumi tiga peringkat utama. Pertama, sampel komposit hibrid Kenaf-Kevlar disediakan menggunakan teknik hand lay-up dengan konfigurasi tiga lapisan Kevlar-Kenaf-Kevlar di dalam acuan berukuran 45 cm x 20 cm, menggunakan resin epoksi sebagai matriks, dan dipotong kepada enam sampel bersaiz 15 cm x 10 cm setelah pengerasan. Seterusnya, sampel yang telah disediakan menjalani ujian terma di mana ia dikenakan sumber pemanasan terkawal dan diperhatikan menggunakan kamera inframerah inframerah bagi mengesan sebarang ketidaksempurnaan dalaman berdasarkan corak taburan haba. Akhir sekali, sampel turut diuji menggunakan kaedah NDT lain seperti radiografi gamma untuk mengesan kecacatan dalaman berasaskan ketumpatan serta teknik laser shearografi bagi menilai keteguhan struktur dan mengenal pasti ketidakseragaman .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7256
melalui analisis corak interferens permukaan. Gabungan ketigatiga pendekatan ini membolehkan penilaian menyeluruh terhadap keutuhan dan sifat dalaman komposit yang dikaji.Penyediaan SampelGambar 1 di bawah menunjukkan sampel komposit hibrid kenaf-Kevlar disediakan menggunakan teknik hand lay-updengan menggunakan acuan berukuran 45 cm x 20 cm. Dalam proses ini, gentian disusun dalam konfigurasi tiga lapisan iaitu Kevlar-Kenaf-Kevlar untuk menghasilkan struktur komposit hibrid. Acuan terlebih dahulu disapu dengan bahan pengasing (release agent) untuk memudahkan proses penanggalan sampel selepas pengerasan. Resin epoksi dicampur bersama agen pengeras dan kemudiannya disapu secara sekata pada setiap lapisan gentian menggunakan berus bagi memastikan peresapan sempurna resin ke dalam gentian. Proses penyusunan dan resinisasi ini dilakukan secara berhati-hati untuk mengelakkan pembentukan gelembung udara. Setelah semua lapisan disiapkan, komposit dibiarkan mengeras pada suhu bilik selama 24 jam. Selepas pengerasan lengkap, plat komposit yang dihasilkan dipotong kepada enam sampel bersaiz 15 cm x 10 cm menggunakan gergaji halus untuk Gambar 1: Penyediaan sampel komposit hibrid menggunakan teknik hand layupSetelah semua lapisan disiapkan, komposit dibiarkan mengeras pada suhu bilik selama 24 jam. Selepas pengerasan lengkap, plat komposit yang dihasilkan dipotong kepada enam sampel bersaiz 15 cm x 10 cm Gambar 2) menggunakan gergaji halus untuk menjalankan ujikaji seterusnyaGambar 2: Plat komposit hibrid yang telah siapUjian TermaUjian kerosakan terma merupakan salah satu kaedah penting dalam penilaian bukan musnah (NDT) untuk mengenal pasti kecacatan dalaman dalam bahan komposit tanpa merosakkan struktur asalnya. Dalam kajian ini, sampel komposit hibrid Kenaf-Kevlar dikenakan kepada rangsangan haba menggunakan sumber pemanasan iaitu udara panas. Pemanasan ini menyebabkan perubahan suhu pada permukaan komposit, dan sebarang kecacatan dalaman seperti delaminasi, rongga udara atau ketidakhomogenan bahan akan mempengaruhi pengaliran haba di kawasan tersebut. Pengimejan Ujian Tanpa Musnah (NDT)Kamera inframerah bertindak dengan mengesan sinaran inframerah yang dipancarkan oleh permukaan bahan yang diuji dan menukarkannya kepada imej suhu atau thermal map. Walau bagaimanapun, untuk memastikan bacaan suhu yang tepat dan imej yang mewakili keadaan sebenar bahan, nilai thermal emissivity bahan perlu diketahui dan dimasukkan ke dalam sistem kamera. Emissivity ialah keupayaan sesuatu bahan untuk memancarkan tenaga haba berbanding dengan badan hitam sempurna (blackbody) pada suhu yang sama. Nilainya berada dalam julat antara 0 hingga 1, di mana nilai 1 mewakili pemancar sempurna (4). Bahan komposit biasanya mempunyai nilai emissivity yang berbeza bergantung kepada jenis gentian, matriks resin, permukaan, dan kemasan akhir.Menentukan nilai emissivity bahan komposit hibrid KenafKevlar adalah penting sebelum ujian termografi dijalankan, kerana kesilapan dalam nilai ini boleh menyebabkan bacaan suhu yang tidak tepat dan seterusnya menjejaskan keupayaan pengesanan kerosakan. Nilai emissivity boleh diperoleh sama ada melalui rujukan literatur, atau lebih baik lagi, ditentukan secara eksperimen menggunakan alat pengukur suhu rujukan dan kamera inframerah pada permukaan sampel yang diuji. Setelah nilai emissivity yang betul diperoleh dan ditetapkan pada sistem kamera, ujian termografi boleh dijalankan dengan lebih tepat untuk mengenal pasti kawasan berpotensi rosak yang menunjukkan anomali dalam pengagihan suhu pada permukaan komposit. Teknik ini sangat berguna untuk pengesanan awal kecacatan tanpa perlu melakukan pembongkaran atau pemotongan struktur, sekaligus menjimatkan masa dan kos dalam penyelenggaraan serta penilaian bahan komposit..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7257
Gambar 3: Eksperimen mengukur nilai emissivity sampelGambar 3 di atas menunjukkan susun atur eksperimen untuk mengukur nilai emissivity sampel. Terdapat dua prosedur untuk mengukur nilai emissivity iaitu kaedah sentuhan dan kaedah tanpa sentuhan mengikut Standard Practice for Measuring and Compensating for Emissivity Using Infrared Imaging Radiometers (ASTM E1933-14). Dalam kajian ini, eksperimen dijalankan bagi mengukur nilai emissivitymenggunakan kaedah sentuhan.Radiografi digital merupakan teknik pengimejan yang menggunakan sinaran gamma atau sinar-X untuk menembusi bahan dan menghasilkan imej dalaman berdasarkan perbezaan ketumpatan. Dalam konteks komposit hibrid, kawasan yang mempunyai kerosakan seperti delaminasi, rongga udara (void), retakan atau kekosongan resin akan menunjukkan kontras yang berbeza dalam imej radiograf kerana perbezaan ketumpatan berbanding kawasan yang utuh. Radiografi digital menawarkan kelebihan dari segi kepekaan tinggi, rakaman imej dalam bentuk digital, dan kemudahan pemprosesan serta analisis imej menggunakan perisian(5).Sementara itu, laser shearografi merupakan teknik interferometri yang sangat sesuai untuk mengesan kecacatan sub-permukaan dalam bahan komposit, terutamanya kecacatan yang berlaku akibat tekanan terma. Teknik ini berfungsi dengan mengesan perubahan kecil dalam bentuk permukaan (deformasi) apabila bahan dikenakan beban kecil, seperti pemanasan ringan (6). Kamera shearography beresolusi tinggi akan merekod corak interferens laser yang dipantulkan dari permukaan bahan, dan kawasan yang mempunyai kecacatan dalaman seperti delaminasi atau lapisan yang terpisah akan menunjukkan pola interferens yang berbeza disebabkan tindak balas mekanikal yang tidak seragam terhadap tekanan terma. Ini membolehkan mengenal pasti kawasan berisiko tanpa perlu menembusi atau merosakkan permukaan bahan.HASIL KAJIANKamera InframerahUntuk mengukur nilai emissivity sesuatu sampel menggunakan kamera inframerah, mulakan dengan menghalakan kamera terma ke arah sampel dan fokuskan pada kawasan di mana emisiviti ingin diukur. Gunakan kamera inframerah yang sesuai, seperti kamera terma berkepekaan tinggi Optrix P640i, untuk mengukur dan membetulkan ralat suhu pantulan yang terkena pada spesimen.Seterusnya, termometer sentuh digunakan untuk mengukur suhu pada titik atau kawasan yang telah diukur menggunakan kamera inframerah tadi. Tanpa mengubah kedudukan kamera,laraskan kawalan emissivity pada perisian kamera terma sehingga paparannya menunjukkan suhu yang sama seperti yang dicatatkan oleh termometer sentuh. Nilai emissivity yang dipaparkan pada ketika itu ialah nilai emissivity sebenar spesimen pada suhu dan julat gelombang spektrum yang ditetapkan. Bagi memastikan ketepatan dan kebolehpercayaan,prosedur ini diulang sebanyak tiga kali dan purata nilai emissivity komposit hibrid Kenaf/Kevlar ialah 0.81.Gambar 4: Ekseperimen Kerosakan TermaSetelah memperoleh nilai emissivity. Ujian kerosakan terma pada komposit hibrid boleh dilaksanakan bagi menilai prestasi bahan komposit hibrid Kenaf-Kevlar apabila terdedah kepada suhu tinggi. Sampel dipasang tegak dengan kemas dan dikenakan udara panas menggunakan heat gun dengan suhu 200°C selama 1 minit. Peralatan ini mensasarkan kawasan tertentu pada permukaan sampel untuk mensimulasikan beban haba secara tumpu. Kamera inframerah diletakkan menghadap permukaan hadapan sampel bagi merakam imej terma secara langsung sepanjang proses pemanasan berlangsung. Kamera inframerah digunakan untuk mengesan perubahan suhu pada permukaan bahan yang mungkin menunjukkan kehadiran kerosakan dalaman seperti delaminasi, kekosongan resin (voids), atau pembakaran tidak seragam. Imej yang dirakam memaparkan taburan suhu secara visual, dan zon panas yang tidak homogen boleh memberi petunjuk kepada kewujudan kecacatan. Walau bagaimanapun, berdasarkan analisis awal (Gambar 5, 6 dan 7), imej terma yang diperoleh tidak menunjukkan perubahan signifikan yang boleh dikaitkan secara langsung dengan kerosakan atau kecacatan dalaman..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7258
Gambar 5: Sebelum Kerosakan Terma, Masa= 0 saat, Suhu=22.4 °CGambar 6: Semasa Kerosakan Terma, Masa= 60 saat, Suhu=201.5 °CGambar 7: Imej akhir (masa penyejukan), Masa=720 saat, suhu =24.2°CSalah satu cabaran utama dalam menganalisis imej yang diperoleh adalah kesukaran dalam pemprosesan lanjut (postprocessing). Perisian asal yang digunakan iaitu Thermofit Pro merupakan versi lama dan menyokong format imej terhad. Hal ini menyukarkan analisis mendalam terhadap perbezaan suhu dan isyarat haba kecil yang mungkin menandakan kecacatanRadiografi Digital dan Laser ShearografiDalam usaha untuk mengesan kecacatan terma pada komposit hibrid kenaf-Kevlar, dua kaedah NDT telah digunakan iaitu radiografi digital dan laser shearografi. Walau bagaimanapun, kedua-dua kaedah ini tidak berjaya menghasilkan imej yang menunjukkan kerosakan terma yang signifikan pada sampel yang diuji. Lihat gambar 8 dan 9.Beberapa faktor dikenal pasti sebagai penyebab utama kepada kegagalan pengesanan ini. Pertama, dari segi komposisi bahan, komposit hibrid yang menggabungkan gentian kenaf dan Kevlar mempunyai profil ketumpatan yang hampir sama. Oleh kerana teknik radiografi bergantung kepada perbezaan ketumpatan untuk menghasilkan kontras imej, ketiadaan kontras imej yang ketara dalam ketumpatan antara dua jenis gentian ini menyebabkan kontras imej yang dihasilkan menjadi terlalu rendah. Ini memberi kesukaran bagi membezakanstruktur dalaman(7), kecacatan, atau taburan gentian dalam komposit.Kedua, bagi kaedah shearografi, penyediaan permukaan sampel memainkan peranan penting dalam memastikan kualiti imej yang baik. Sekiranya terdapat ketidaksamarataan pada lapisan permukaan atau pencemaran seperti sisa resin, ia boleh mengganggu keseragaman pantulan laser dan seterusnya menjejaskan kejelasan corak gangguan (interference pattern)yang direkodkan. Dalam kes ini, kemungkinan wujud ketidakkonsistenan dalam penyediaan permukaan yang menyebabkan imej tidak jelas atau tidak menggambarkan kecacatan dengan ketara (6).Akhir sekali, sensitiviti teknik juga perlu diambil kira. Kerosakan terma yang diinduksi mungkin terlalu kecil atau terletak pada kedalaman yang tidak dapat dikesan oleh sistem pengimejan yang digunakan. Ini bermaksud bahawa tahap kecacatan berada di luar julat kepekaan peralatan radiografi digital dan shearografi yang digunakan, sekali gus menyukarkan pengesanan awal kerosakan dalaman.Gambar 8: Imej Radiografi komposit hibrid sebelum (kiri) dan selepas (kanan) kerosakan terma.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7259
Gambar 9: Imej Shareography komposit hibrid sebelum (kiri) dan selepas (kanan) kerosakan termaKONKLUSISebagai kesimpulan, kajian ini menunjukkan bahawa pengesanan kecacatan terma pada komposit hibrid kenafKevlar menggunakan teknik kamera inframerah, radiografi digital dan laser shearografi menghadapi beberapa cabaran dari segi sensitiviti teknik, persamaan ketumpatan bahan, serta penyediaan permukaan sampel. Hasil pengimejan tidak menunjukkan perubahan signifikan yang boleh dikaitkan dengan kerosakan terma, sekali gus menandakan keperluan untuk penambahbaikan dalam metodologi yang digunakan. Untuk kajian masa hadapan, disarankan agar teknik NDT yang lebih sensitif seperti ultrasonik fasa array atau computed tomography (CT) industri digunakan bagi mengatasi batasan kontras dan sensitiviti. Selain itu, peningkatan dalam aspek penyediaan sampel, seperti kawalan permukaan dan pemanasan yang lebih terkawal, serta penggunaan perisian pemprosesan imej moden seperti MATLAB dengan sokongan algoritma pengesanan automatik, boleh membantu dalam mempertingkatkan kejelasan dan ketepatan dalam mengenal pasti kecacatan dalaman komposit. Pendekatan bersepadu ini dijangka dapat memberi gambaran lebih menyeluruh terhadap keutuhan struktur komposit hibrid yang diuji.RUJUKAN[1] Yusuff, I., Sarifuddin, N., & Ali, A. M. (2021). A review on kenaf fiber hybrid composites: Mechanical properties, potentials, and challenges in engineering applications. Progress in Rubber, Plastics and Recycling Technology, 37(1). https://doi.org/10.1177/1477760620953438[2] Shireesha, Y., Nandipati, G., & Chandaka, K. (2019). Properties of hybrid composites and its applications: A brief review. In International Journal of Scientific and Technology Research (Vol. 8, Issue 8).[3] Razali, N., Sultan, M. T. H., & Jawaid, M. (2017). A review on detecting and characterizing damage mechanisms of synthetic and natural fiber based composites. In BioResources (Vol. 12, Issue 4). https://doi.org/10.15376/biores.12.4.Razali[4] Zhu, C., Hobbs, M. J., & Willmott, J. R. (2020). An accurate instrument for emissivity measurements by direct and indirect methods.Measurement Science and Technology, 31(4). https://doi.org/10.1088/1361-6501/ab5e9b[5] Lyra, M. E., Kordolaimi, S. D., & Salvara, A. L. N. (2010). Presentation of digital radiographic systems and the quality control proceduresthat currently followed by various organizations worldwide. In Recent Patents on Medical Imaging (Vol. 2, Issue 1). https://doi.org/10.2174/1877613201002010005[6] Yusof, M. Y., Loganathan, T. M., Burhan, I., Abdullah, W. S. W., Mohamed, M. N. I., Abidin, I. M. Z., & Zakaria, N. (2019). Shearography Technique on Inspection of Advanced Aircraft Composite Material. IOP Conference Series: Materials Science and Engineering, 554(1). https://doi.org/10.1088/1757-899X/554/1/012009[7] RADIOGRAPHIC EXAMINATION PROCEDURE AS NON DESTRUCTIVE TESTING METHOD IN PROCESS PIPING. (2019). MECHA JURNAL TEKNIK MESIN. https://doi.org/10.35439/mecha.v2i1.5.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7260
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.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7.DMLDQ$QDOLVD%LEOLRPHWULNffl3HQHOLWLDQArtificialIntelligence $, GDQ,QRYDVL3HQGLGLNDQ/DWLKDQ7HNQLNDO 9RNDVLRQDO 79(7 Sufandi Mohd Johan1*, Lim Tian Pau2, Hairul Azam Mohd Mokhtar3, Deenesh Kumar Nalathambi4, & Dzulkhazmi Lutpi51,2,3,4,5Jabatan Kejuruteraan Mekanikal, Politeknik Port Dickson, 71050 Si Rusa, Negeri Sembilan, [email protected]$%675$. ó Wacana global mengenai Revolusi Perindustrian 4.0semakin ketara menggunakan teknologi moden dalam dunia pendidikan dan salah satunya adalah Artificial Intelligence (AI). Artificial Intelligence (AI) direkabentuk untuk membantu menyelesaikan masalah atau tugasan manusia melalui mesin dan program komputer. Kemunculan Artificial Intelligence (Al) menjadi pemangkin penguasaan teknologi, kemahiran teknikal, digital sistem,keselamatan siber, pembangunan mampan dan kerjasama industri, seterusnya menjadi sasaran utama pendidikan TVET. Tujuan kajian ini adalah menganalisa penerbitan artikel berkaitan kesan Artificial Intelligence (AI) di bidang Pendidikan dan Latihan Teknikal & Vokasional. Metodologi yang digunakan adalah analisis bibliometrikdeskriptif dari database dari laman web Dimensions bermula tahun 2016 hingga 2025 dengan sejumlah 208 artikel. Hasil data kajianmenunjukkan, pada tahun 2024 jumlah publikasi berkaitan AI adalah tertinggi, iaitu sejumlah 38 artikel. Analisa visualisasi bibliometrik yang dihasilkan VOSviewer menggambarkan landskap penyelidikan antarabangsa antara negara yang mengaplikasi Artificial Intelligence(AI). Berdasarkan kajian ini, South Africa dan lesotho menjadi hab pusat inovasi dan keluaran akademik dalam Artificial Intelligence (AI)dan Industri 4.0, khususnya berkaitan pembangunan tenaga kerja dan transformasi digital. Penerbitan AI didominasi oleh Universiti Johannesburg di kawasan Benua Afrika, manakala di Malaysia dipelopori oleh Universiti Tun Hussein Onn. Visualisasi peta VOSviewer menggariskan keperluan segera untuk sistem TVET lebih responsif dengan menggabungkan kecekapan AI, literasi data dan kurikulum yang fleksibel agar pelajar bersedia dengan pertumbuhanindustri yang berkembang pesat. Kesimpulan penyelidikan ini membuka pandangan luas pendidikan untuk meningkatkan kualitipengajaran dan menyediakan tenaga pengajar ke arah revolusi pendidikan 4.0. Oleh itu langkah institusi untuk memodenkan bidang TVET, dengan cara mengadaptasi Artificial Intelligence (AI) dengan pendidikan vokasional bukan sahaja penting bagi ekonomi negara tetapi bakal meningkatkan kualiti pengetahuan, revolusi teknologi global dan pembangunan lestari negara lebih berkesan.Kata Kunci ó %LEOLRPHWULN 'LPHQVLRQV 9RVYLHZHU ArtificialIntelligence $, ,QQRYDWLRQ 7HNQRORJL +DU]LQJ¶V RI 3HULVK 3R3 3HQGLGLNDQ79(7I. PENGENALANRevolusi perindustrian ini, diwarnai oleh automasi, jaringan, pembelajaran pintar, digital dan analisis data langsung, telah mengubah permintaan tenaga kerja buruh merentas pelbagai sektor dengan ketara (UNESCO, 2023). Walaupun TVET telah secara tradisinya tertumpu kepada kemahiran praktikal, pasaran buruh semasa memerlukan graduan untuk memiliki kemahiran digital yang unggul dan kebolehsuaian kepada teknologi (ILO, 2024). Mengintegrasikan kecerdasan buatan (AI) ke dalam program TVET telah menjadi tumpuan utama untuk merapatkan jurang kemahiran ini, mewujudkan peluang berharga untuk meningkatkan pembelajaran berasaskan amalan sambil melengkapkan pelajar untuk kerjaya yang berkemahiran AI (McKinsey, 2023). Penggabungan teknologi AI ke dalam Pendidikan Teknikal dan Vokasional dan Latihan (TVET) ialah pemangkin yang berkuasa, mengubah pembangunan kemahiran, penglibatan pelajar dengan sumber pendidikan, dan meningkatkan kebolehpasaran (Zary, A., & Zary, N., 2025). Pendidikan adalah satu lagi bidang yang menawarkan potensi luar biasa untuk aplikasi teknologi AI (Guan, C, Mou, J., & Jiang, Z., 2020). Malah, inovasi AI dalam pendidikan telah berkembang daripada blueprint yang ideal kepada konteks pembelajaran kehidupan sebenar dengan lebih kompleks. Firma dalam industri teknologi pendidikan (EdTech) telah membangunkan sistem pembelajaran kesesuaian individu (adaptive learning) yang membenarkan pembelajaran peribadi (personalized learning), sistem pengajaran berbantu yang membantu pengurusan persekitaran bilik kuliah. Teknologi pendidikan juga membantu sistem pemarkahan, penilaian dan masalah pengurusan bahasa dan sistem pentadbiran institusi yang membantu dengan pendaftaran pelajar dan kaunter pertanyaan menggunakan AI (Adeoye, M. A, & Otemuyiwa, B. I., 2024). Pendidikan sentiasa menjadi aspek penting dalam pembangunan di setiap lapisan masyarakat (Strielkowski, W. et al., 2025). Ia juga telah mengalami banyak perubahan mendalam dari semasa ke semasa yang diperlukan untuk mencerminkan cabaran terkini seperti kemajuan teknologi hijau atau kesan rumah hijau dan perubahan iklim (Acour & Alenezi, 2022; Alenezi, 2023). Pada masa kini, ketika kita memasuki era baharu pasca COVID yang menandakan transformasi selepas pandemik (2020ñ2023) kepada endemik, teknologi pembelajaran adaptif dan kecerdasan buatan (AI) sedang merevolusikan pendidikan dan ia berperanan dalam pembangunan pendidikan untuk kelestarian tidak pernah 270
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7berlaku seperti sebelum ini (Adiguzel et al., 2023; de Vries, 2022; Jing et al., 2023). Penyelidikan artificial intelligence dan innovation TVET sudah pasti menjadi peranan penting dalam pendidikan pada tahun-tahun mendatang menjadi cabaran dan peluang pembangunan mampan bidang pendidikan serta penyelidikan (Pedro, F., et al., 2019). Dengan peningkatan bilangan dalam penyelidikan berkaitan artificial intelligencedalam inovasi pendidikan seiring bersama cabarannya khusus dalam bidang pendidikan TVET. Atas sebab ini, adalah perlu untuk menggunakan kaedah statistik untuk menganalisis hasil kajian ini bagi mengetahui fokus penyelidikan dan mencari kebaharuan penyelidikan. Analisis bibliometrik adalah kaedah yang bernilai untuk menganalisa penerbitan artikel (Muhammad et al., 2023). Bibliometrik ialah kaedah statistik yang mengandungi pelbagai maklumat tentang penyelidikandalam kajian tertentu (Muhammad et al., 2022). Dengan mendapatkan maklumat ini daripada penerbitan berkaitan artificial intelligence dalam pendidikan TVET melalui pangkalan data penyelidikan. Penyelidikan menerusi analisa bibliometrik yang berkaitan tema artificial intelligence dengan pengajian tinggi dan TVET adalah seperti kajian yang dijalankan oleh (Sahar, R., & Munawaroh, M., 2025; Mohamad, S. A., 2025; Wicaksono et al., 2021) berkaitan penyelidikan artificial intelligence secara umumnya dalam bidang pendidikan vokasional yang bersumberkan penerbitan pangkalan data scopus. Analisis bibliometrik yang diterangkan dalam kajian ini memberikan maklumat yang signifikan dengan tema utama bidang artificial intelligence dalam pengajaran pendidikan vokasional, yang boleh dilihat dalam pencapaian pembelajaran, peningkatan daya tahan motivasi, penambahbaikkan berterusan, kelesterian pendidikan, pembelajaran adaptif, analisa ramalan, kecekapan pengurusan, inovasi, kemahiran dan fokus pengajar dalam aktiviti pengajaran dan pembelajaran di pengajian tinggi. Oleh itu, kajian ini dicadangkan agar dilaksanakan penyelidikan lebih menyeluruh membincangkan artificial intelligence dalam bidang inovasi TVET sebagai tema kajian. Berikut adalah objektif kajian ini adalah untuk menganalisa fokus kajian dan kebaharuan yang dikaitkan dengan artificial intelligence dalam konteks inovasi TVET (Muhammad et al., 2023). Penyelidik menggunakan pangkalan data dimensions untuk menganalisis penerbitan artikel dari tahun 2016 hingga 2025 dengan membincangkan persoalan kajian berikut:1. Bagaimanakah taburan bidang penerbitan artificialintelligence dan inovasi TVET khususnya di kalanganpenyelidik di universiti?2. Apakah corak dalam penerbitan dan petikan berkaitan bidangartificial intelligence dan inovasi TVET? Bagaimanakah trendberkaitan dengan artificial intelligence dan inovasi TVET?3. Apa perspektif pengajaran telah muncul dalam bidang ini?Apakah yang didedahkan oleh pengedaran ini tentangkepentingan penerbitan ini dalam komuniti akademik?4. Apakah inovasi teknologi yang sesuai diterapkan dalamTVET untuk menyokong pendidikan kelestarian, dansustainable development melalui penggunaan AI?5. Apakah bidang tumpuan khusus dalam penyelidikan yangberkaitan dengan pembelajaran penemuan baharu dalamartificial intelligence dan inovasi TVET?6. Perkara menjadi cabaran dan kelemahan atau kesan negatifdari data penyelidikan mengenai artificial intelligence daninovasi TVET?II. METHODOLOGIKajian ini menggunakan kaedah analisis deskriptif bibliometrik (Shah, Lei, Ali, Doronin, & Hussain, 2020). Kaedah kajian analisis bibliometrik menjawab persoalan kajian dengan melihat perkembangan penyelidikan dan literatur. Sampel dalam kajian ini ialah 40 penerbitan yang diperoleh daripada pangkalan data dimensions yang sepadan dengan kata kunci yang ditetapkan. Penyelidik menggunakan pangkalan data dimensions untuk mencari sumber data yang berkaitan dengan Artificial Intelligence (AI) dan Innovation TVET kerana liputan antara disiplinnya yang luas. Terdapat beberapa langkah dalam menyempurnakan data yang telah dikumpul. Yang pertama ialah pengenalan tema, saringan, penilaian kelayakan dan akhirnya, analisa data yang terpilih (Moher et al., 2009; Sovacool et al., 2022). Proses pengumpulan data ditunjukkan dalam Rajah 1.Dalam Rajah 1, dapat dilihat bahawa langkah pertama dalam proses pengumpulan data ialah proses pengenalpasti data. Penyelidik memasukkan kata kunci dalam carian pada pangkalan data dimensions. Kata kunci yang dimasukkan ialah ìartificial intelligence\" AND \" A.I\" AND \"TVET\" AND \"innovation\". Aliran penerbitan dan aliran petikan yang berkaitan dengan artificial intelligence dalam innovation TVET diasingkan mengikut tahun bermula dari 2016 hingga 2025. Hasil proses mengenalpastian memperoleh data penerbitan sebanyak 208 artikel. Langkah seterusnya ialah proses saringan. Iaitu penyelidik membuat saringan mengikut kriteria. Iaitu, penerbitan adalah bukan buku dan mestilah dalam bentuk artikel yang diterbitkan dalam jurnal. Daripada hasil saringan ini, 64 penerbitan telah diperolehi yang memenuhi kriteria di atas. Ini bermakna 144 penerbitan telah diasingkan dan tidak diteruskan dalam proses seterusnya. Penyelidik membuat pemilihan secara manual berkaitan penerbitan yang layak untuk dimasukkan ke dalam peringkat seterusnya. Penyelidik melihat abstrak dan tajuk 64 penerbitan dan menilai penerbitan yang mengandungi atau memasukkan pembolehubah artificial intelligence dalam innovation TVET dan penerbitan mestilah dalam bahasa Inggeris. Pada akhir fasa ketiga ini, 40 penerbitan telah diperolehi yang layak untuk dimasukkan ke peringkat seterusnya. Fasa kelayakan melibatkan penilaian kebolehlaksanaan penerbitan yang dipilih. Pada peringkat ini, penyelidik menetapkan kriteria khusus, iaitu hanya memilih artikel sama ada mengandungi istilah khusus artificial intelligence dalam innovation TVET untuk melayakkan diri ke fasa analisa data. 271
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Data yang diambil daripada pangkalan data dimensions akan disimpan dalam dua format berbeza. Pertama, disimpan dalam format CSV dan kedua, disimpan dalam format RIS. Fail CSV dianalisis menggunakan perisian VOSviewer, manakala fail RIS diproses melalui perisian Harzing's Publish atau Perish serta Mendeley desktop untuk penilaian selanjutnya.III. KEPUTUSAN DAN PERBINCANGANTrend penerbitan berkaitan artificial intelligence dalam innovation TVET dijalankan secara analisis deskriptif menggunakan analisis bibliometrik. Bilangan penerbitan dan barisan aliran penerbitan kelihatan meningkat setiap tahun dari 2016 hingga 2025 akan dipaparkan dalam graf menggunakan perisian Microsoft excel seperti rajah 2. Data yang ditunjukkan dalam rajah 2 jelas menunjukkan bahawa 2024 adalah tahun kemuncak untuk penerbitan dalam bidang khusus ini. Pada tahun 2020, tiga puluh dua artikel telah diterbitkan, menandakan peningkatan yang ketara berbanding tahun-tahun sebelumnya. Lonjakan paling ketara berlaku antara 2018 dan 2020, dengan bilangan penerbitan meningkat lapan kali ganda dalam tempoh ini.Rajah 1: Carta proses data diperolehiRajah 2: Dokumen perkembangan artificial intelligence dan innovation TVET272
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Berdasarkan gambar diatas, satu negara mempunyai 2 dokumen mengenai artificial intelligence dalam innovation TVET. Seperti yang dihujahkan oleh (Bai et al., 2019), bilangan petikan kertas yang diberikan cenderung bukan sahaja memastikan nilai keseluruhan sumbangannya (saintifik atau praktikal), tetapi juga memberi kesan kepada aspek lain yang lebih berkaitan dengan pasukan penyelidik. Daripada analisis data mengenai penelitian artificial intelligence dalam innovation TVET, semua penerbitan yang wujud mencapai agregat 188 petikan. Berdasarkan data yang dibentangkan dalam gambar 3, bilangan petikan dalam 11 teratas negara yang paling banyak memetik artikel ini ialah 647 petikan, dengan south africa (188), poland (170), Malaysia (87), dan UK (45) merupakan negara yang paling banyak memetik penerbitan yang sama. Kajian mendapati negara south africa mendapat faedah yang besar dengan kecenderungan penggunaan algoritma, berdasarkan keputusan data dan artificial intelligncedalam pentadbiran negara termasuk pendidikan tinggi (Patel, S., & Ragolane, M. 2024). Total Link Strength (TLS) mengukur kekuatan perhubungan (pautan) dengan negara lain dalam rangkaian. Ia adalah kuantitatif lebih tinggi TLS lebih kuat atau lebih banyak sambungan kerjasama penyelidikan. Setiap pautan mewakili hubungan pengarang bersama antara penyelidik dari negara yang berbeza. Petikan baersama pengarang atau institusi dari negara tersebut. Menurut (Kondo, T.S., & Diwani, S.A 2023) Negara south africa dan lesotho menjadi negara paling banyak pengaruh gan negara lain di benua afrika. Penyelidikan ini mendedahkan pandangan penting, termasuk pengarang utama, jurnal dan penerbit yang berpengaruh, negara yang mempunyai produktiviti penyelidikan tertinggi, sumber pembiayaan dan sokongan kerajaan yang patut diberi perhatian, organisasi berpengaruh dan domain penyelidikan lazim di negara itu. Perkara ini disokong dari kajian (Abdullah, M. I., et al., 2025) menyatakan bahawa automasi pentadbiran dalam institusi membawa manafaat kepada keperluan AI sebagai contoh Industri di TAFE Queensland dan institusi Politeknik Nanyang. Namun di Malaysia, bilangan penyelidik masih berada di tahap rendah disebabkan, terdapat perbezaan dalam akses kepada teknologi canggih ini merentasi yang berbeza wilayah, berpotensi memburukkan lagi ketidaksamaan sedia ada dalam hasil pendidikan (Mohamad, A.F.B., et al., 2024) dalam kajian sebelum ini. Penyelidikan mengenai artificial intelligence dalam innovationTVET dari 2016 hingga 2025 telah dikategorikan kepada lima kelompok berbeza menggunakan aplikasi VOSviewer. Kelompok ini menyerlahkan titik fokus penyelidikan dalam bidang ini sepanjang tempoh yang ditetapkan. Dalam rajah 4, terdapat sejumlah 41 item yang dikategorikan kepada lima warna berbeza dalam aplikasi VOSviewer. Warna-warna ini mewakili bahagian dalam fokus penyelidikan yang berkaitan dengan artificial intelligence dalam innovation TVET sepanjang tempoh tersebut. Bidang fokus pertama dilambangkan dengan bulatan merah yang merangkumi 12 item. Antaranya, kata kunci dengan diameter bulatan terbesar ialah application, continuous improvement, educator, teacher, place dan potential solution, menunjukkan bahawa application, educator dan aspek berkaitan continuous improvement adalah fokus utama bidang ini. Fokus penyelidikan kedua diwakili oleh bulatan hijau, yang terdiri daripada 9 item. Dalam kategori ini, sustainability, sustainable development dan technological innovation, memberi kesan dengan lebih besar bulatan, menandakan kepentingannya sebagai subjek utama fokus penyelidikan kedua. Bidang penekanan penyelidikan yang paling terkini ditunjukkan oleh bulatan biru, yang terdiri daripada 9 item. Antaranya, kata kunci education outcome, adaptive learning dan personalized learning mempunyai yang terbesar bulatan, menekankan bahawa kaedah pengajaran telah menjadi tumpuan utama penyelidikan terkini dalam bidang ini.Rajah 3: Negara kekerapan petikan (citation) antara negara273
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Pemetaan 41 kata kunci menggunakan Vosviewer permohonan menunjukkan pembentukan enam kelompok yang berbeza, sebagai terperinci dalam Jadual 1. Kluster 1 mengandungi empat item utama termasuk analytic, application, continuous improvement, educator, era, future direction, intelligent tutoring, issue, place, potential solution, teacher, urgent need. Kelompok ini menggariskan tematik penumpuan pengajar antara metodologi mengintegrasikan artificial intelligencedalam innovation TVET dan kemampanan ke dalam TVET adalah penting untuk menyediakan tenaga kerja yang bersedia untuk masa hadapan. Memberi tumpuan pendekatan AI dan kemampanan dalam TVET, mengkaji arah aliran, cabaran dan pendekatan strategik yang muncul (Ishrat, M., et al 2025). Era teknologi AI telah membawa kepada ilmu baharu, era yang penuh dengan potensi untuk merevolusikan dan meningkatkan pengalaman pengajaran dan pembelajaran menjadi landskap dalam pendidikan terbaik (Rosyadi, M.I., 2023). Pelaksanaan AI yang cenderung ke arah perubahan pendidikan dan langkah strategik bagi peningkatan kecekapan pengajaran, kaedah pembelajaran personalized learning yang lebih meluas. Kluster 2 fokus kepada pembangunan mampan dengan cara teknologi baru yang perlu disepadukan dalam program TVET termasuk: penggunaan papan pintar, chatbot, teknologi Web 2.0 seperti Blog, Wiki dan alat Penanda Halaman Sosial. Sementara itu bantuan kewangan kepada institusi TVET oleh pihak berkepentingan harus dipandang serius dan tidak boleh dipinggirkan atau disalah guna supaya ia boleh digunakan dengan berkesan untuk pemerolehan dan penyediaan teknologi baru untuk pengajaran dan pembelajaran (Okanya, V., 2023). Kluster 4 bertemakan item yang menunjukkan bahawa pengetahuan dan kecekapan AI dalam kalangan pelajar menjadi tumpuan dengan cara perlu lebih banyak kursus AI ditawarkan untuk dipelajari oleh pelajar. Dalam erti kata lain, kurikulum TVET perlu dikemaskini dan kurikulum TVET berasaskan AI perlu diperkenalkan. (Mustapha, R., et al., 2023).IV. PERBINCANGANRajah 4: Visualisasi rangkaian kata kunci dan fokus penyelidikanIV. PERBINCANGANJadual 1: Kelompok kluster dibentuk dari kata kunci274
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7%DJDLPDQDNDKartificial intelligenceGDQinnovation79(7NKXVXVQ\\DGLNDODQJDQSHQ\\HOLGLNGLXQLYHUVLWL\"Berdasarkan rajah 5, South Africa menjadi hab pusat inovasi dan keluaran penerbitan dalam Artificial Intelligence (AI) dan Industri 4.0, khususnya berkaitan pembangunan tenaga kerja dan transformasi digital. University of Johannesburg dari benua Afrika meneraju pengetahuan artificial intelligence (AI) dan perkara ini peluang dalam mengubah sistem pendidikan tinggi dan menyumbang secara signifikan untuk mencapai Matlamat Pembangunan Mampan (SDG) (Opesemowo, O. A. G., & Adekomaya, V. 2024). Teknologi dinamik AI memberi manafaat untuk memajukan SDG dalam sistem pendidikan tinggi Afrika Selatan. Hasil dapatan kajian mendapati penggunaan teknologi AI di institusi pendidikan tinggi Afrika Selatan secara meluas. Manakala penerbitan AI di Malaysia dipelopori oleh Universiti Tun Hussein Onn Pensyarah (UTHM). Di kalangan penyelidik UTHM, perbincangan kumpulan penyelidik berfokus kepada penyelidikan sains komputer (CS) dengan menghasilkan bilangan tertinggi iaitu sebanyak 2095 telah diterbitkan sebagai kertas persidangan; diikuti dengan 957 kertas dalam kategori artikel (Kaur, S., Ibrahim, R., & Selamat, A. (2014). Hasilnya menunjukkan bahawa penyelidik menggunakan teknologi AI untuk meningkatkan pembelajaran dan penglibatan pelajar supaya pelajar memberi perhatian dalam kuliah. Telah didapati bahawa teknologi AI telah meningkatkan peluang untuk pembelajaran kolektif (Ntsobi, M. P., et al., 2024). Kajian itu seterusnya membuktikan bahawa teknologi AI boleh meningkatkan pengalaman pembelajaran yang diperibadikan pelajar dengan gaya dan kebolehan pembelajaran yang pelbagai. Ini telah membawa kepada persekitaran bilik kuliah yang lebih inklusif dan interaktif di mana pelajar berasa lebih bermotivasi dan disokong dalam perjalanan pembelajaran mereka (Adeoye, M. A., & Otemuyiwa, B. I. 2024). Penyepaduan teknologi AI ke dalam pendidikan telah menunjukkan hasil yang memberangsangkan dalam meningkatkan hasil pelajar dan memupuk suasana pembelajaran yang lebih kolaboratif. Berdasarkan keputusan, membayangkan bahawa memanfaatkan AI akan memajukan SDG di institusi pendidikan tinggi Afrika Selatan (Opesemowo, O. A. G., & Adekomaya, V. (2024).$SDNDK FRUDN GDODP SHQHUELWDQ GDQ SHWLNDQ EHUNDLWDQELGDQJ DUWLILFLDO LQWHOOLJHQFH GDQ LQQRYDWLRQ 79(7\"%DJDLPDQDNDK WUHQG LQL EHUNDLWDQ GHQJDQ DUWLILFLDOLQWHOOLJHQFHGDQLQQRYDWLRQ79(7\"Untuk melihat trend petikan yang berkaitan dengan bidang ini, penyelidik menyusun penerbitan berdasarkan bilangan petikan yang boleh dilihat dalam Jadual 2.%LO 3HQJDUDQJ WDKXQ 7DMXN 3HWLNDQ FLWDWLRQ (Hassan, R.H., et al 2021)ICT Enabled TVET Education: A Systematic Literature Review22 (Kenayathulla, H.B 2021)Are Malaysian TVET graduates ready for the future?11 (Adnan, A.H.M., et al 2020)Industry 4.0 critical skills and career readiness of ASEAN TVET tertiary students in Malaysia, Indonesia and Brunei10 (Rawat, R.S., et al 2022)Complaint Management in Ethiopian Vocational and Technical Education Institutions: A Framework and Implementation of a Decision Support System6 (Chairani, V.S., et al2018)Literature review: Some of TVET area will be eliminated due to industrial revolution 4.0, is that true?4 (Omeh, C.B., et al 2025)Application of artificial intelligence (AI) technology in tvet education: Ethical issues and policy implementation4Rajah 5: Hubungan antara penerbitan tajuk tema dikalangan penyelidik universiti275
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Menurut (Hasan, R.H., et al 2021) dalam kitaran latihan pendidikan artificial intelligence dan innovation TVET, tahap keupayaan ICT perlu dikaji semula. Menerusi kajiannya menilai literatur untuk inovasi ICT dalam pendidikan TVET untuk jangka masa sepuluh tahun yang lalu. Hasil dapatan kajian bahawa teknologi ICT dan penyebaran aplikasi ke dalam komponen sistem kitaran latihan TVET/bidang fungsi adalah sangat rendah terutamanya dalam pemantauan dan penilaian, bimbingan kerjaya dan penempatan kerja, penilaian pelatih, dan latihan guru. Indeks Teknologi TVET mencadangkan bahawa banyak tumpuan diperlukan pada IoT, Robotik, Sains Data, Kepintaran buatan, pengkomputeran awan dan induksi teknologi serupa yang lain kepada semua latihan TVET. Sementara itu, menurut (Kenayathulla, H.B.,2021) landskap pekerjaan berubah begitu pantas, keupayaan untuk bersedia untuk memperpelbagaikan kemahiran graduan masa depan. Graduan mestilah mencari memanfaatkan peluang yang diberikan dalam laluan pembangunan graduan berkemahiran Revolusi Industri 4.0. Tambahan pula menerusi kerangka kerja Sistem Latihan Dual Nasional telah dilaksanakan oleh Kementerian Sumber Manusia bagi memastikan graduan TVET dilengkapi dengan kemahiran kebolehpasaran yang diperlukan dalam pasaran kerja masa kini. Untuk tujuan itu, firma perlulah menilai tahap kemahiran dan kebolehan yang diperolehi oleh graduan yang telah melalui Sistem Latihan Dual Nasional sama ada berkesan atau perlu ditambah baik (Mohamad, A. F. B.,2024). Firma perlu segera mengenal pasti kemahiran kebolehpasaran yang diperlukan untuk memenuhi permintaan pekerjaan dalam Revolusi Industri 4.0. dengan maklum balas firma tersebut memberikan pandangan kepada penggubal dasar dan pemaju kurikulum mengenai kerangka TVET yang responsif yang perlu diambil untuk memastikan graduan kami dilengkapi dengan kemahiran kebolehpasaran yang dikehendaki masa hadapan negara (Subrahmanyam, S., 2025).$SDSHUVSHNWLISHQJDMDUDQWHODKPXQFXOGDODPELGDQJLQL\"$SDNDK \\DQJ GLGHGDKNDQ ROHK SHQJHGDUDQ LQL WHQWDQJNHSHQWLQJDQSHQHUELWDQLQLGDODPNRPXQLWLDNDGHPLN\"Berdasarkan rajah 6 dibawah, penggunaan peralatan teknologi artificial intelligence dan innovation TVET dan rangka kerja pengajaran ke arah personalized learning dalam TVET menunujukkan meningkatkan motivasi pelajar, mengalakkan persediaan kerjaya pelajar dan meningkatkan hasil pembelajaran mereka (Ujah, C.O., 2024). Namun perlu perkemaskan lagi strategi penyelidikan pelaksanaan personalized learning agar lebih berkesan dan lestari. Penyelidikan berterusan bagi mewujudkan dasar peranan AI dalam TVET yang diperlukan disebabkan masalah menangani etika AI, kebolehcapaian dan keberkesanan dalam pendidikan berasaskan kemahiran (Baako, I., 2025). Di antara strategik AI dalam pendidikan teknikal dan vokasional, dicadangkan rangka kerja komprehensif yang merangkumi penilaian keperluan, garis panduan etika, pembangunan fakulti, pelaksanaan berulang, penglibatan pihak berkepentingan, dan pemantauan dan penilaian berterusan. Rangka kerja ini bertujuan untuk mengoptimumkan penggunaan AI dalam menyediakan pelajar untuk tenaga kerja yang berkembang sambil menangani cabaran yang berpotensi dan memastikan pengalaman pembelajaran yang seimbang dan berkesan (Ali, T. M. A., et al., 2024). Menurut (Lim, S.C.J., & Lee, M.F.,2024) telah membuat kajian mengenai era AI iaitu meneroka graduan pendidikan teknikal yang diperlukan dalam kemahiran AI untuk memupuk daya saing tenaga kerja masa hadapan dan kejayaan perniagaan dalam masa era AI. Penyelidik menyerlahkan peranan pendidikan dan latihan teknikal dan vokasional (TVET) dalam menyediakan pekerja masa depan untuk tempat kerja digital, iaitu, inisiatif kemahiran semula dan peningkatan kemahiran yang boleh membantu individu mencapai kemahiran dan kecekapan teknikal yang diperlukan untuk menyesuaikan diri dengan persekitaran yang berubah (Amdan, M. A. B., 2025). Kami juga membincangkan beberapa strategi pendidikan yang berpotensi yang memanfaatkan manfaat AI dalam pendidikan, seperti proses pembelajaran yang bersesuaian, boleh diakses dan mengikut kehendak kebolehan pelajar.Rajah 6: Hubungan antara personalized learning dan application276
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7$SDNDK LQRYDVL WHNQRORJL \\DQJ VHVXDL GLWHUDSNDQ GDODP79(7 XQWXN PHQ\\RNRQJ SHPEDQJXQDQ GD\\D WDKDQSHQGLGLNDQ GDQ VXVWDLQDEOH GHYHORSPHQW PHODOXLSHQJJXQDDQ$,\"Berdasarkan rajah 7 dibawah, pendidikan artificial intelligence dan innovation TVET amat diperlukan untuk membina modal insan berkemahiran yang penting untuk daya saing ekonomi dan meningkatkan perpaduan sosial. Hasil kajian mendapati AI dalam TVET memberi impak positif kepada prospek pekerjaan, mengurangkan kos dan memupuk inovasi, membayangkan bahawa perkongsian dan dasar yang diselaraskan harus diteruskan untuk mengoptimumkan manfaat AI dan menyumbang kepada pembangunan ekonomi yang mampan (Abdullah, M. I., et al., 2025). Pendidik dapat memberikan pelajar kemahiran untuk berjaya dalam teknologi hijau, pembuatan pintar, tenaga boleh diperbaharui, dan bidang baru yang lain. Pengintegrasian ini memberdayakan individu dengan kemahiran baru dan menyumbang kepada ekonomi global yang lebih lestari dan berdaya tahan. Penjelajahan lanjut mungkin dapat merapatkan jurang antara kemajuan teknologi dan tanggungjawab alam sekitar. Ia menyokong pendidik dalam menavigasi kerumitan mengintegrasikan AI dan kelestarian ke dalam latihan vokasional (Azar, A.S., et al., 2025). Mengintegrasikan AI ke dalam TVET memastikan kelestarian dengan membekalkan pelajar dengan kemahiran penting untuk menavigasi ekosistem digital yang semakin kompleks selain meneroka hubungan simbiotik AI dan menyoroti kesannya terhadap model pendidikan yang lestari. Di samping itu inovasi kurikulum, aplikasi dunia nyata, dan kerangka strategik untuk menyelamatkan kelestarian pembangunan berasaskan AI ke dalam TVET (Regula, K. C. (2025). Bilangan Institusi Pendidikan Teknikal dan Vokasional (TVET) semakin memanfaatkan artificial intelligence dan big data untuk meningkatkan pengagihan sumber, mengoptimumkan pengambilan keputusan, dan memperbaiki hasil pendidikan. Dengan menganalisa big data analitik yang didorong oleh AI dan rumusan sintesis big data dapat digunakan untuk mempermudahkan reka bentuk kurikulum, mempersonalisasikan pengalaman pembelajaran, dan meningkatkan kecekapan operasi dalam sistem TVET (Mahesh, P., 2025). Mengikut strategi berasaskan data, bagaimana pemodelan ramalan, automasi pintar, dan analitik maju dapat mendorong penggunaan sumber yang cekap dari segi kos dan tapi memberi impak yang tinggi. Di antara perkara menjadi cabaran iatu pertimbangan etika, dan arah masa depan untuk mengintegrasikan AI dan big data ke dalam TVET, memastikan pertumbuhan yang lestari dan inklusif dalam pembangunan kemahiran dan kesiapan tenaga kerja (Mittoor, G. K. R., & Putteti, S. (2025). $SDNDKELGDQJWXPSXDQNKXVXVGDODPSHQ\\HOLGLNDQ\\DQJEHUNDLWDQGHQJDQSHPEHODMDUDQSHQHPXDQEDKDUXGDODPDUWLILFLDOLQWHOOLJHQFHGDQLQQRYDWLRQ79(7\"Berdasarkan rajah 8, ilustrasi kemajuan terkini dan trend penyelidikan dalam artificial intelligence dan innovation TVET, diwakili oleh kata kunci baharu yang diserlahkan dalam pelbagai warna. Peralihan minat penyelidikan ini menunjukkan usaha berterusan untuk memperdalam pemahaman kita tentang kepentingan artificial intelligence dalam pendidikan. Meneroka kata kunci mewakili pendidik seperti \"educator,\" continuous improvementî, \"urgen need,\" \"intelligent tutoring\" ìanalyticî dan \"potential solution\" menawarkan potensi untuk mendedahkan cerapan tentang pengaruh faktor seperti \"student\" dan \"relationship\" terhadap pembelajaran masih belum dikaji. Walaupun kemunculan kata kunci baharu ini mencadangkan kemajuan, adalah penting untuk mengakui bahawa bidang itu mungkin masih dalam peringkat pembangunan. Penyelidikan komprehensif lanjut diperlukan untuk mendapatkan pemahaman yang lebih terperinci tentang artificial inteligence dalam pendidikan. Menyedari batasan dan kelemahan kajian terdahulu kekal penting dalam membentuk usaha penyelidikan masa depan. Dalam segitiga merah, tema utama, artificial Rajah 7: Hubungan antara personalized learning dan applicationRajah 8: Kata kunci terbaharu dan hubungan antara kata kunci277
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7intelligence, belum lagi saling berkaitan dengan kata kunci lain seperti \"motivation,\" \"outcome education,\" \"adaptive learning\", ìalgorithmî, ìdataî ìstem educationî dan \"engagement.\" Ini menandakan aspek yang belum diterokai dalam penyelidikan artificial intelligence antara pelaksanaan AI dalam TVET menghadapi beberapa perkara belum diungkai kerana ia menjejaskan keselamatan data dan privasi data melalui algoritma yang memerlukan sistem canggih dan kos dana yang besar mungkin disebabkan kekurangan kemahiran digital (Deckker, D., & Sumanasekara, S. 2025). Bakal pendidik dalam latihan boleh menggunakan prinsip bekerjasama antara pendidik berpengalaman dan penggubal dasar bersama dengan wakil industri untuk mengoptimumkan potensi nilai artificial intelligence dalam pendidikan vokasional (Mahesh, P., 2025). Di samping itu, penyelidikan ini juga mengenal pasti bidang aplikasi AI yang mungkin untuk meningkatkan hasil pendidikan dalam TVET dan STEM dan kemungkinan halangan untuk pelaksanaan yang berkesan. Penemuan ini memberikan gambaran keseluruhan asas pengetahuan pada masa ini tentang penggunaan AI dalam sektor pengajian tinggi, dengan makna yang berkaitan untuk pendidik dan institusi yang sedang mempertimbangkan untuk membenamkan AI ke dalam kurikulum. Hasilnya akan lebih menyedari bagaimana AI boleh digunakan untuk mengecilkan jurang antara cara tradisional mendidik orang ramai mengenai permintaan tenaga kerja yang semakin digital, dengan itu meningkatkan kebolehpasaran graduan TVET dan STEM dengan ketara di Malaysia (Amdan, M. A. B., et al., 2025). Pendidikan STEM Bersepadu dalamTVET untuk menyelaraskan Pendidikan dan Latihan Teknikaldan Vokasional (TVET) dengan permintaan pasaran buruh yangsemakin berkembang. Hasil kertas kajian mengenai sistemTVET dan pendidikan STEM menerusi perbincangan pakar,rangka kerja dasar serantau seperti sebagai Pelan Kerja ASEANmengenai Pendidikan 2021ñ2025 dan Pelan StrategikSEAMEO 2021ñ2030. Inisiatif ini memacu penjajaran dasar,memupuk perkongsian awam-swasta, dan melaksanakanpedagogi inovatif. Melalui kerjasama serantau dan amalanterbaik, SEAMEO STEM-ED dan ILO berhasrat untukmemupuk tenaga kerja yang berdaya tahan, bersedia untukmenyokong pembangunan mampan dan pertumbuhan ekonomidalam era digital (Sriboonruang, O., & Somsaman, K., 2025).3HUNDUDPHQMDGLFDEDUDQGDQNHOHPDKDQDWDXQHJDWLIKDVLOGDSDWDQ GDUL GDWD SHQ\\HOLGLNDQ PHQJHQDL DUWLILFLDOLQWHOOLJHQFHGDQLQQRYDWLRQ79(7\"Menurut (Yee, C.Z., 2024) beberapa maklum balas yang dikumpul daripada tinjauan yang dijalankan dengan pakar industri yang berpengalaman bertahun-tahun dalam industri bekerja dan pengalaman menyelia pelatih dan graduan baru: ìPelajar TVET iniÖ Saya melihat bahawa mereka hanya mengajar kemahiran yang sukar dilaksanakan. Keupayaan mereka untuk berinteraksi dan berkomunikasi sangat lemahÖbahasa badan mereka tidak sesuai.î ìKebanyakan fresh graduate belum mempunyai kemahiran kepimpinan iniî. Teknologi pembelajaran mestilah penglibatan pelajar dan memberikan maklum balas masa dengan segera, memastikan pendidikan berasaskan kecekapan seperti VR dan AR menawarkan latihan praktikal, manakala kelayakan digital mengesahkan kemahiran graduan jelas berkelayakan. Namun begitu teknlogi itu menghadapi cabaran seperti privasi data, eksploitasi algoritma dan ekuiti digital, integrasi AI tidak beretika dalam pendidikan yang inklusif (Grech, A., & Camilleri, A. F. 2020; Faisal, S. M., Khan, W., & Ishrat, M. (2025). Di samping itu juga penekanan yang semakin meningkat terhadap kelestarian dan telah menaikkan keperluan untuk kemahiran hijau atau kemahiran khusus yang mengintegrasikan kesedaran alam sekitar serta penguasaan teknologi. Namun masalah dari segi kurikulum yang ketinggalan zaman dan akses yang tidak sama rata dan tidak komprehensif untuk membangunkan kemahiran hijau bagi memupuk ekonomi lestari dan kerjaya yang mencabar (Subrahmanyam, S. (2025). Kajian keberkesanan AI pula mendedahkan antara lain bahawa ketersediaan alatan AI tidak mencukupi untuk menyokong pembelajaran kemahiran pelajar secara berkesan dimana sumber alatan harus tersedia dengan pelaksanaan AI dalam program TVET di institusi awam (Ukala, C. C., & Iheukwumere, O. C. E. (2025). Namun begitu,penggunaan yang berjaya adalah bergantung kepada mengatasihalangan utama, termasuk teknologi jurang celik huruf,sokongan institusi, dan pertimbangan etika. Analisis statistikmenunjukkan bahawa jangkaan prestasi dan keadaanmemudahkan adalah penentu penting bagi niat tingkah lakupelajar untuk menerima pakai AI (Chatterjee, S., &Bhattacharjee, K., 2020; Baharin, A. T., 2024). Penyelidikberpendapat bahawa teknologi digital masih peringkat rendah,apabila diambil secara global, masih mempunyai potensi palingbesar untuk transformasi sektor TVET dalam jangka pendek.Namun TVET digital meningkat ketara dalam kos semakinmeningkat dan kecanggihan tawaran. Pendigitalan dilihatdengan keraguan oleh segmen penting penubuhan pendidikandan institusi TVET khususnya memerlukan kecekapan digitalkeseluruhan pendidik dan jurulatih akan terus menjadi faktorpengehad utama dalam kemampuan TVET digital yangsemakin meningkat dalam tempoh lima tahun akan datang(Grech, A., & Camilleri, A. F. (2020). Sejak kebelakangan ini,program TVET tidak digemari oleh kebanyakan pelajarMalaysia kerana beberapa faktor seperti minat pelajar,pengaruh ibu bapa, tanggapan negatif industri, kemudahan diinstitusi vokasional, pengajar TVET yang tidak berpengalaman,dan pelbagai pandangan negatif masyarakat. Akibatnya, ia akanmenimbulkan isu kekurangan pekerja mahir. Semua ini bolehmenjurus kepada isu kritikal akan meluas, menyebabkanekonomi kita akan menurun, oleh itu pihak agensi TVETbertanggungjawab mesti melaksanakan secara berkesan strategiuntuk menghidupkan dan memberi inspirasi kepada pelajaruntuk menyertai program TVET (Hong, C. M., 2023). Implikasikurikulum dan sukatan pelajaran TVET, mempromosikansektor TVET dan kerjasama industri, penerimaan teknologi 4.0sebagai alat pembelajaran, dan meningkatkan kapasitipensyarah TVET berkemahiran yang dikenal pasti penting278
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7untuk pelbagai industri dalam era 4.0 untuk melengkapkan graduan TVET dengan secukupnya untuk bekerja dalam era 4.0 (Magagula, M. M., & Awodiji, O. A. (2024). V. KESIMPULANBerdasarkan keputusan dan perbincangan tema artificial intelligence dan innovation TVET, dapat disimpulkan bahawa penerbitan telah meningkat daripada 7 pada tahun 2016 kepada 38 pada tahun 2024. Analisa visualisasi bibliometrik yang dihasilkan VOSviewer menggambarkan landskap penyelidikan antarabangsa antara negara yang mengaplikasi Artificial Intelligence (AI). Berdasarkan kajian ini, South Africa dan lesotho menjadi hab pusat inovasi dan keluaran akademik dalam Artificial Intelligence (AI) dan Industri 4.0, khususnya berkaitan pembangunan tenaga kerja dan transformasi digital. Visualisasi peta VOSviewer menggariskan keperluan segera untuk sistem TVET lebih responsif dengan menggabungkan kecekapan AI, literasi data dan kurikulum yang fleksibel agar pelajar bersedia dengan pertumbuhan industri yang berkembang pesat. Kesimpulan penyelidikan ini membuka pandangan luas pendidikan untuk meningkatkan kualiti pengajaran dan menyediakan tenaga pengajar ke arah Pendidikan 4.0. Mengintegrasikan AI dan kelestarian dalam pendidikan dan Latihan Teknikal dan Vokasional (TVET) memberikan panduan menyeluruh kerangka kerja, strategi, amalan terbaik, dan cabaran yang berkaitan dengan transformasi ini.RUJUKANAbdullah, M. I., Hajamydeen, A. I., & Johar, M. G. M. (2025). Transforming TVET with AI: Economic Benefits of Technological Innovation in Education. Online Journal for TVET Practitioners, 10(1), 69-86.Adeoye, M. A., & Otemuyiwa, B. I. (2024). Navigating the Future: Strategies of EdTech Companies in Driving Educational Transformation. JERIT: Journal of Educational Research and Innovation Technology, 1(1), 43-50.Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152.Akour, M., & Alenezi, M. (2022). Higher Education Future in the Era of Digital Transformation. Education Sciences, 12(11), 784. https://doi.org/10.3390/educsci12110784.Alenezi, M. (2023). Digital Learning and Digital Institution in Higher Education. Education Sciences, 13(1), 88. https://doi.org/10.3390/educsci13010088.Ali, T. M. A., Ahmed, A. A., & Alsharif, A. (2024). Improving the Educational Process in Technical and Vocational Education Using Artificial Intelligence: Innovative Strategies and Tools. Afro-Asian Journal of Scientific Research (AAJSR) (AAJSR), 796-707.Amdan, M. A. B., Janius, N., Saidin, M. S. B., & Kasdiah, M. A. H. B. (2025). Impact of Artificial Intelligence in TVET and STEM Education among Higher Learning Students in Malaysia. Journal of Research in Mathematics, Science, and Technology Education, 2(1), 1-14.Azar, A.S., Gupta, S.K., Taherdoost, H., Alhamaty, F. (2025) Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET)24 April 2025, IGI Global, Pages 1-377, DOI: 10.4018/979-8-3373-1142-5.Baako, I. (2025). A Bibliometric investigation of Artificial Intelligence in Technical and Vocational Education Training (AI-TVET): Trends and insights for a decade.Baharin, A. T., Sahadun, N. A., Ramli, S., & liyana Redzuan, N. A. (2024). Exploring the Adoption of Generative Artificial Intelligence by TVET Students: A UTAUT Analysis of Perceptions, Benefits, and Implementation Challenges.Bai, X., Zhang, F., Lee, I.: Predicting the citations of scholarly paper. J. Informetr. 13(1), 407ñ418 (2019).Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463.Deckker, D., & Sumanasekara, S. (2025). AI in vocational and technical education: Revolutionizing skill-based learning. EPRA International Journal of Multidisciplinary Research (IJMR), 11 (3), 9. https://doi. org/10.36713/epra20462.de Vries, P. (2022). The Ethical Dimension of Emerging Technologies in Engineering Education. Education Sciences, 12(11), 754. https://doi.org/10.3390/educsci12110754.Faisal, S. M., Khan, W., & Ishrat, M. (2025). AI-Driven Pedagogy and Assessment in TVET: Transforming Education Through AI-Enhancing Pedagogy and Assessment in TVET. In Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET) (pp. 127-150). IGI Global Scientific Publishing.Grech, A., & Camilleri, A. F. (2020). The digitization of TVET and skills systems.Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134-147.279
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Hong, C. M., Chíng, C. K., Roslan, N., & Raihana, T. (2023).Predicting Studentsí Inclination to TVET Enrolment Using Various Classifiers. Pertanika Journal of Science & Technology, 31(1).ILO (2024). Skills for a resilient future: Technical and vocational education and training in a changing world of work.Ishrat, M., Khan, W., Faisal, S. M., Ansari, M. S. H., & Ahmad, F. (2025). Future Trends and Challenges for AI and Sustainability in TVET. Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET), 1-32.Jam, N. A. M., & Puteh, S. (2020). Developing a conceptual framework of teaching towards education 4.0 in TVET institutions. In International Conference on Business Studies and Education (ICBE) (pp. 74-86).Jing, Y., Zhao, L., Zhu, K., Wang, H., Wang, C., & Xia, Q. (2023). Research Landscape of Adaptive Learning in Education: A Bibliometric Study on Research Publications from 2000 to 2022. Sustainability, 15(4), 3115. https://doi.org/10.3390/su15043115.Kaur, S., Ibrahim, R., & Selamat, A. (2014, June). Research productivity in computer science field for Universiti Teknologi Malaysia in 2000ñ2013. In 2014 International Conference on Computer and Information Sciences (ICCOINS) (pp. 1-6). IEEE.Kivindu, D. G. (2024). Embedding Artificial Intelligence in Skills Development in Kenyan TVET Institutions. THE KENYA JOURNAL OF TECHNICAL AND VOCATIONAL EDUCATION AND TRAINING VOL: 7, 30.Kondo, T. S., & Diwani, S. A. (2023). Artificial intelligence in Africa: a bibliometric analysis from 2013 to 2022. Discover Artificial Intelligence, 3(1), 34.Lim, S. C. J., & Lee, M. F. (2024). Rethinking education in the era of artificial intelligence (AI): Towards future workforce competitiveness and business success. In Emerging Technologies in Business: Innovation Strategies for Competitive Advantage (pp. 151-166). Singapore: Springer Nature Singapore.Magagula, M. M., & Awodiji, O. A. (2024). The implications of the fourth industrial revolution on technical and vocational education and training in South Africa. Social Sciences & Humanities Open, 10, 100896.Mahesh, P. (2025). Theoretical Foundations of AI and Sustainability in TV. In Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET) (pp. 61-82). IGI Global Scientific Publishing.McKinsey & Company. (2023). Closing the skills gap: Preparing TVET for Industry 4.0.Mittoor, G. K. R., & Putteti, S. (2025). From Data to Action: Optimizing Resources in TVET Through AI and Big Data Insights. In Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET) (pp. 177-188). IGI Global Scientific Publishing.Mohamad, A. F. B., Suhaimin, M. K. N. B., Akmal, S. B., Rahim, H. R. B. A., Mahmood, W. H. B. W., & Hashim, H. S. B. (2024, December). Enhancing TVET GraduateEmployability Through AI Integration: An AHP Analysis inthe End-of-Life Vehicle (ELV) Sector. In 2024 InternationalConference on TVET Excellence & Development (ICTeD)(pp. 59-64). IEEE.Mohamad, S. A., Zaharudin, R., Ngelambong, A., Wan-ZainalShukri, W. H., & Munusamy, I. (2025). Shaping the Future: Bibliometric Insights on Teaching Strategies in Vocational Education. International Journal of Research and Innovation in Social Science, 9(3), 1673-1686.Moher, D., A. Liberati, J. Tetzlaff, D. G. Altman, and for the PRISMA Group, ìPreferred reporting items for systematic reviews and meta-analyses: the PRISMA statement,î BMJ, vol. 339, no. jul21 1, pp. b2535ñb2535, Jul. 2009, doi: 10.1136/bmj. b2535.Muhammad, I., Marchy, F., Do, A., & Naser, M. (2023). Analisis Bibliometrik: Tren Penelitian Etnomatematika dalam Pembelajaran Matematika Di Indonesia (2017 - 2022). JIPM (Jurnal Ilmiah Pendidikan Matematik.a), 11(2), 267-279. https://doi.org/10.25273/jipm.v l li2.14085.Muhammad, I., Marchy, F., Rusyid, H. K., & Dasari, D. (2022). Analisis Bibliometrik: Penelitian Augmented Reality Dalam Pendidikan Matematika. JIP M (Jurnal Ilmiah Pendidikan Matematika), 11(1), 141-155. https://doi.org/10.25273/jipm.v l lil .13818Mustapha, R., Rosly, N. I., Yasin, A. A., Lambin, R., Saad, F.,& Kashefian, S. (2023, June). Knowledge and competency of vocational teacher trainees in the field of artificial intelligence (AI): A case study in a Malaysian public university. In AIP conference proceedings (Vol. 2750, No. 1, p. 040074). AIP Publishing LLC.Ntsobi, M. P., & Mwale, B. J. Revolutionising Teaching and Learning Through AI: A Case Study of South Africa. Asian Journal of Social Science and Management Technology ISSN: 2313-7410 Volume 6, Issue 5, September-October, 2024 Available at www.ajssmt.com.Okanya, V. (2023). Enhancing Integration of Emerging Technologies in Technical Vocational Education and Training (TVET) Programmes for Sustainable Development. INDUSTRIAL TECHNOLOGY EDUCATION RESEARCH JOURNAL, 6(1), 73-85.280
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Opesemowo, O. A. G., & Adekomaya, V. (2024). Harnessing artificial intelligence for advancing sustainable development goals in South Africa's higher education system: A qualitative study. International Journal of Learning, Teaching and Educational Research, 23(3), 67-86.Patel, S., & Ragolane, M. (2024). The implementation of artificial intelligence in South African higher education institutions: Opportunities and challenges. Technium Education and Humanities, 9, 51-65.Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.Ramli, S., Rasul, M. S., & Affandi, H. M. (2019). The importance of green skills-from the perspective of TVET lecturers and teacher trainees. International Journal of Innovation, Creativity and Change, 7(6), 186-199.Regula, K. C. (2025). AI and Cybersecurity: A Symbiotic Relationship for Sustainable Technical and Vocational Education. In Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET) (pp. 299-318). IGI Global Scientific Publishing.Rosyadi, M. I., Kustiawan, I., Tetehfio, E. O., & Joshua, Q. (2023). The role of AI in Vocational Education: A systematic literature review. Journal of Vocational Education Studies, 6(2), 244-263.Sahar, R., & Munawaroh, M. (2025). Artificial intelligence in higher education with bibliometric and content analysis for future research agenda. Discover Sustainability, 6(1), 1-32.Shah, S. H. H., Lei, S., Ali, M., Doronin, D., & Hussain, S. T. (2020). Prosumption: bibliometric analysis using HistCite and VOSviewer. Kybernetes, 49(3), 1020ñ1045Sovacool, B. K., Daniels, C., & AbdulRafiu, A. (2022). Science for whom? Examining the data quality, themes, and trends in 30 years of public funding for global climate change and energy research. Energy Research & Social Science, 89(4), 1ñ20.Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI!driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921-1947.Sriboonruang, O., & Somsaman, K. (2025). ADVANCING INTEGRATED STEM EDUCATION IN TECHNICAL AND VOCATIONAL EDUCATION AND TRAINING (TVET). SOUTHEAST ASIAN JOURNAL OF STEM EDUCATION, 2.Subrahmanyam, S. (2025). Developing Green Skills for Sustainable Careers. In Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET) (pp. 101-126). IGI Global Scientific Publishing.Wicaksono, A.G., Sunamo, W., Ashadi, & Adi Prayitno, B. (2021). Research Trends of Discovery Leaming from 2015 to 2019: A Bibliometric Analysis. Journal of Physics: Conference Series, 1842(1). https://doi.org/10.1088/1742- 6596/ 1842/ l/0 12026Ujah, C. O. (2024). Evaluating the Effectiveness of Personalized Learning Approaches in TVET Colleges. International Journal of Home Economics, Hospitality and Allied Research, 3(2), 152-167.Ukala, C. C., & Iheukwumere, O. C. E. (2025). Integrating Artificial Intelligence (Ai) In Technical And Vocational Education And Training In Public (TVET) Institutions In Abia State, Nigeria: Bridging Skills Gaps For Future Workforce.UNESCO. (2023). Digital transformation of TVET: Harnessing technology to support teaching and learning in technical and vocational education.Yee, C. Z. (2024) Technological Unemployment and the Future of and Work in Malaysia.Zary, A., & Zary, N. (2025). Artificial Intelligence in Technical and Vocational Education and Training: Empirical Evidence, Implementation Challenges, and Future Directions.281
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7[1]'HYHORSPHQWRI*UDSKLFDO8VHU,QWHUIDFHIRU((*%UDLQZDYH'DWD$FTXLVLWLRQKhairul Azha A AzizFakulti Teknologi dan Kejuruteraan Elektronik dan Komputer,Universiti Teknikal Malaysia Melaka Melaka, [email protected] ZakariaAdvanced Technology Training Centre (ADTEC) JTMTangkak Campus, acronyms acceptableTangkak, [email protected]²This project focuses on the development of a GraphicalUser Interface (GUI) using MATLAB for real-time acquisition and visualization of EEG brainwave signals. EEG data is collected using a commercial brainwave sensor such as NeuroSky or MindLink and transmitted via an Arduino microcontroller to MATLAB through serial communication. The data acquired from the sensor are raw brainwave signal and converted into Neurosky data value. The GUI was successfully developed to display live EEG signals, providing a userfriendly platform for real-time monitoring and basic signal analysis. Validation shows that the data displayed in MATLAB matches the original signal from the device. The system lays a foundation for future EEG-based applications in research, education, and healthcare.KeywordsóGraphical User Interface, Data Acquisition, EEG, BCI I. INTRODUCTIONAn EEG (electroencephalography) data collecting system would normally be utilised in a hospital setting to monitor brain activity. Typically, electrodes are attached to the scalp in order to detect the tiny electrical currents that the brain's neurons produce. In the hospital context, EEG is frequently used to identify and keep track of neurological diseases such epilepsy, brain tumours, and head injuries.An electrogram of the brain's spontaneous electrical activity can be captured via electroencephalography (EEG). It has been demonstrated that the biosignals picked up by EEG are postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. The International 10-20 system, or variants of it, is commonly used to implant the EEG electrodes along the scalp, making it a minimally invasive procedure. The term \"intracranial EEG\" is occasionally used to refer to electrocorticography, which involves surgically implanting electrodes. Visual inspection of the trace or quantitative EEG analysis are the two methods used most frequently in clinical interpretation of EEG.A healthy human EEG will display specific patterns of activity that are related to a person's level of alertness. One may detect frequencies between 1 and 30 Hz, and amplitudes can range from 20 to 100 V. The detected frequencies are classified into several groups, including theta (0.5ñ4 Hz), alpha (8ñ13Hz),beta (13ñ30 Hz), and delta (0.5ñ4 Hz) (4-7 Hz).With the growing interest in brain-computer interface (BCI) technology and the increasing availability of consumer-grade EEG devices, there is a need for practical tools that facilitate real-time data acquisition and visualization. Graphical user interfaces (GUIs) developed using platforms such as MATLAB can bridge the gap between raw EEG signal acquisition and meaningful interpretation, enabling researchers, educators, and developers to interact with brain signals in an accessible and efficient manner. This project aims to develop such a GUI system to support EEG data acquisition for diverse applications beyond the clinical environment.II. METHODOLOGYThis research employs a real-time brain-computer interface (BCI) system consisting of three primary components: a NeuroSky MindWave EEG headset, an Arduino Uno microcontroller, and a personal computer (PC) running MATLAB software. The system is designed to enable seamless acquisition, transmission, and interpretation of EEG signals for interactive and creative content applications. The overall structure of the system is shown in Figure 1.282
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Figure 1: Block Diagram of the EEGñArduinoñMATLAB SystemThe first stage of the system involves EEG signal acquisition using the NeuroSky MindWave Mobile headset. This device is a non-invasive, single-channel EEG sensor designed to capture electrical activity from the human brain, particularly at the Fp1 location of the frontal lobe. Figure 2, Bluetooth module and Arduino uno which are combined to send and receive signals from the patient The headset includes built-in analog and digital filtering to remove noise and artifacts such as eye blinks or muscle movements. It processes the raw EEG signals and transmits simplified cognitive metrics such as attention, meditation, and poor signal level via Bluetooth communication using the proprietary ThinkGear protocol. These data packets are encoded in a structured byte format suitable for decoding by microcontrollers or PCs.Figure 2: Neurosky Mindlink EEG SensorThe second stage focuses on signal interfacing using Arduino Uno. An HC-05 Bluetooth module is connected to the Arduinoís UART (TX/RX) pins to receive the EEG data in realtime. Connection between the Arduino and the Bluetooth module as shown at Figure 3. These parsed values are then sent to the personal computer via USB using the Arduinoís serial communication port, set at a baud rate of 9600 bps. This stage serves as the hardware bridge between the EEG source and the software processing environment.Figure 3: Bluetooth Module Connection to ArduinoThe final stage of the system involves data processing and visualization on a PC using MATLAB. MATLAB is chosen for its powerful capabilities in data acquisition, graphical user interface (GUI) design, and real-time plotting. A custom MATLAB script is developed to read EEG data from the serial port using the serialport() object and readline() functions. Once the attention and meditation values are extracted, they are plotted in real-time using dynamic visualization components such as plot, drawnow, and GUI elements created using App Designer or GUIDE. In addition, the MATLAB application allows users to apply digital filters (e.g., Butterworth or moving average filters), log data into .csv files for future analysis, and generate interactive feedback such as visual effects or sound modulation. This enables the use of EEG signals in creative media applications including music control, digital art interaction, or stress monitoring systems. Figure 4 shows the time series waveform from Bonn Dataset.Figure 4: Examples of Time Series in Bonn DatasetIn conclusion, this methodology presents a modular, lowcost approach to brain-computer interfacing using consumergrade EEG equipment and open-source tools. By integrating NeuroSky hardware with Arduino and MATLAB, the system supports real-time EEG data capture and creative content development, making it suitable for applications in interactive design, digital art, and cognitive-based user experience systems.III. DATA FLOW AND COMMUNICATIONThe data flow between components in this system is designed to enable efficient, real-time signal acquisition, transmission, and visualization (Smith, J., 2023). EEG signals are initially collected by the NeuroSky MindWave headset, which detects brainwave activity from the userís forehead using dry electrodes or the Mindlink brain sensor (Johnson & Lee, 2022). These signals, indicative of cognitive states, are internally processed by the headset to produce key metrics such as attention and meditation levels (NeuroSky, 2021).Once acquired, the EEG data is wirelessly transmitted from the headset to the Arduino microcontroller via Bluetooth communication, utilizing the ThinkGear protocol (Garcia et al., 2020). This protocol organizes the EEG data into predefined packet formats optimized for parsing by embedded systems (Kumar & Singh, 2019). Upon reception, the Arduino decodes 283
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7these packets, filters out poor-quality signals, and extracts the relevant metrics (Tanaka, 2022).Subsequently, the Arduino sends the processed EEG metrics to a personal computer through serial communication via a USB interface (Wong & Patel, 2021). Data transmission occurs at a baud rate of 9600 bps to maintain a stable and continuous stream (Brown, 2020). On the PC, a MATLAB application receives the data through the serial port, processes the numerical values, and visualizes them in real-time (Chen & Park, 2023). This graphical user interface (GUI) dynamically reflects changes in the userís cognitive state, providing immediate feedback and supporting interactive applications such as creative art, music generation, or cognitive-based control systems (Lee, D., 2025). Figure 5 shows Flowchart of EEG Signal Acquisition, Transmission, Processing, and RealTime Visualization Using NeuroSky MindWave, Arduino, and MATLAB GUI.Figure 5: Flowchart Data FlowIV. GUI DESIGN IN MATLABThe use of a graphical user interface (GUI) in real-time EEG monitoring systems offers significant advantages in usability, interactivity, and data visualization. In this project, the GUI was developed using MATLABís App Designer, which provides a robust environment for designing professional applications with support for drag-and-drop components and object-oriented programming, enabling rapid and scalable interface development (MathWorks, 2023). This development approach facilitates real-time monitoring of EEG signals, particularly from the NeuroSky headset, thereby enhancing user interaction and delivering intuitive feedback aligned with brainwave activity (Chakladar et al., 2021).A central feature of the GUI is the Start/Stop button, which allows users to initiate or terminate the data acquisition process. This control is tightly integrated with MATLABís serial communication routines, enabling seamless connection with the Arduino microcontroller at the onset of data transmission and ensuring safe disconnection upon terminationóminimizing data loss and hardware issues (GonÁalves et al., 2022).The core of the interface includes Live Plot Axes that render continuous real-time waveforms of attention and meditation metrics. This visualization not only enhances the interpretability of brain activity but also supports immediate assessment of cognitive states, which is crucial for neurofeedback and training scenarios (Zhao et al., 2020).To support detailed monitoring, the GUI incorporates a Raw Value Display panel that provides real-time numerical feedback on NeuroSkyís attention and meditation indices. This feature is particularly valuable in research contexts that demand precise and quantitative data tracking (Sweeney et al., 2012). Furthermore, a Status Panel is implemented to indicate the connectivity status between the EEG headset, Arduino, and PC, as well as to display the data acquisition rate. This element ensures that users remain informed of communication integrity and are promptly alerted to delays, interruptions, or anomalies in data transmission (AlarcÛn et al., 2021).V. SIGNAL PROCESSINGThe advantages of the signal processing methodology employed in this study lie in its structured approach, which ensures reliable interpretation of EEG signals for further analysis and application (Teplan, 2002). The process begins with the acquisition of raw data from the EEG device, which often contains noise and artifacts due to environmental and physiological factors (Fatourechi et al., 2007). To enhance data quality, the signals are filtered to remove unwanted noise, effectively isolating meaningful neural information (He et al., 2004). Proprietary algorithms developed by NeuroSky are then applied to extract specific EEG frequency bands, such as alpha and beta waves, which serve as indicators of various cognitive and mental states (NeuroSky Inc., 2011). Additionally, the processed data is analyzed using techniques like averaging to observe overall trends and threshold-based alerting to detect significant deviations in brainwave activity (Sanei & Chambers, 2007). This methodology enables objective, accurate extraction of cognitive markers, thereby supporting more robust evaluations of mental states and neurophysiological responses (Pfurtscheller & Lopes da Silva, 1999). Figure 6 shows signal processing methodology. 284
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Figure 6: EEG Signal Processing MethodologyVI. RESULTSGUI to carry out the signal reception process from the EEG Sensor and carry out the TEST process to convert the waveform of the signal to a voltage value so that it is easy to read compared to the signal wave graph. The resulting GUI as shown at Figure 7 contains eight graph displays that show the obtained signal and eight displays that will show the obtained signal voltage. There are two buttons that function to start the operation, first the RUN button which will start the process of receiving the signal from the EEG Sensor and the second the STOP button which will stop the operation of converting the graph signal to voltage unit and making a comparison operation and then displaying the final result of the analysis that has been made.Figure 7: GUI System EEGSuccess in the implementation of the GUI on MATLAB shown in Figure 7 is very easy for the user because only three steps are used in the GUI Software to obtain results such as entering data values, pressing the RUN button to start the operation and get the result. In the GUI, the Software also shows the types of graphs obtained in real time so that the user can clearly see the current state of the patient's brain and can also read the voltage received from the EEG Sensor. Overall, the graphical user interface for EEG has been successful in producing a product that can apply data acquisition techniques and successfully acquire EEG data from the user.VII. DISCUSSIONA comparison between the data obtained from the Arduino Serial Monitor and the real-time waveform plotted in MATLAB confirms that the transmitted EEG signals retain high fidelity. The waveform captured through the GUI matches the original sensor signal, indicating that the system maintains reliable communication and accurate signal representation throughout the acquisition and visualization pipeline. Figure 8 shows the data received from the EEG sensor, which is then read by MATLAB for further processing.Figure 8: Data from serial monitorThe interface was designed with user-friendliness in mind. Users are only required to follow three simple steps: input data values, click the RUN button to begin signal acquisition, and observe the results. This simplicity makes the system highly suitable for educational and experimental environments where users may not have deep technical expertise.The success of this single-channel EEG GUI opens up multiple future possibilities. Applications may include stress monitoring, attention tracking in educational settings, cognitive load analysis, or real-time neurofeedback. Furthermore, the system provides a foundation for expansion through the integration of machine learning models or multi-channel EEG sensors for more sophisticated analysis and pattern recognition.The conversion of the GUI into an executable (.exe) file significantly enhances its portability and usability. Users can run the application on systems without MATLAB installed, making it more practical for wider deployment in classrooms, workshops, or clinical pilot studies.VIII. CONCLUSIONThe development of a MATLAB-based GUI for EEG brainwave data acquisition successfully demonstrates a reliable, real-time platform for displaying and analyzing brain activity using commercial headsets. This project shows that low-cost EEG hardware combined with an open-source microcontroller and a Graphical User interface based on MATLAB can serve as an effective educational and research tool. Future enhancements could include multi-channel EEG support, integration with machine learning models, and advanced signal processing features for cognitive state classification. The 285
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7system lays a practical foundation for EEG-based applications in brain-computer interfaces, mental health monitoring, andneurofeedback systems.REFERENCES[1] Aderibigbe, S. A., Mohammed, S. J., Khalid, M. W., &Others (2025). Artificial Intelligence-Driven Prediction OfOptimal Technology-Aided Alternative Operations InPost-Emergency Contexts: A Case Study From An EmiratiUniversity. International Journal Of Educational ResearchOpen, 9, 0-0. https://doi.org/10.1016/j.ijedro.2025.100473[2] Lee, D. (2025). International Journal of EducationalTechnology in Higher Education, 22(1), 0-0.https://doi.org/10.1186/s41239-025-00503-7[3] Zhao, Y., Qiao, H., Shi, Y., & Liu, H. (2020). Real-timeEEG signal classification for BCI-based controlapplications. IEEE Access, 8, 10462ñ10470.https://doi.org/10.1109/ACCESS.2020.2965316[4] He, B., Musha, T., Okamoto, Y., Homma, S., Nakajima, Y.,& Sato, T. (2004). Electric source imaging of brain activityusing EEG: advances in spatiotemporal resolution. CriticalReviews in Biomedical Engineering, 32(5ñ6), 419ñ454.[5] Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Eventrelated EEG/MEG synchronization and desynchronization:basic principles. Clinical Neurophysiology, 110(11),1842ñ1857.[6] Fatourechi, M., Bashashati, A., Ward, R. K., & Birch, G.E. (2007). EMG and EOG artifacts in brain computerinterface systems: A survey. Clinical Neurophysiology,118(3), 480ñ494.https://doi.org/10.1016/j.clinph.2006.10.019[7] NeuroSky Inc. (2011). ThinkGear CommunicationsProtocol. NeuroSky White Paper.[8] Sanei, S., & Chambers, J. A. (2007). EEG SignalProcessing. John Wiley & Sons.[9] Smith, J. (2023). Real-time EEG signal monitoring usinglow-cost devices and MATLAB interface. BiomedicalSignal Processing Review, 14(2), 134ñ145.[10] Garcia, P., Rahman, M., & Tan, C. (2020). OptimizingBluetooth-based EEG transmission for Arduino. Journal ofEmbedded Systems and Robotics, 5(1), 45ñ53.[11] Chakladar, A., Majumdar, R., & Ghosh, T. (2021).Development of MATLAB GUI for real-time EEGmonitoring: A practical guide. International Journal ofBiomedical Engineering, 9(1), 10ñ18.[12] Kumar, V., & Singh, R. (2019). EEG-based cognitive statedetection using Arduino and MATLAB. ProcediaComputer Science, 152, 51ñ58.https://doi.org/10.1016/j.procs.2019.05.007[13] GonÁalves, J., Pinto, C., & Costa, D. (2022). Enhancingneurofeedback training using GUI-based EEGapplications. Journal of Neural Engineering andInformatics, 10(3), 88ñ97.[14] AlarcÛn, M., S·nchez, F., & Moreno, J. (2021). ArduinoMATLAB integration for biomedical signal visualization.International Journal of Electronics and BiomedicalEngineering, 9(4), 67ñ74.[15] Teplan, M. (2002). Fundamentals of EEG measurement.Measurement Science Review, 2(2), 1ñ11.286
Industri Peralihan Tenaga dan Nadir Bumi
Sustainable Biobased TAS for Energy - Efficient Perovskite Solar Cells1 00XVWDID066X¶DLW)1-XPDDK 0<RVKL]DZD)XMLWD1$/XGLQ1++DVVDQ .0&KDQ1Advanced National Youth Skills Institute (IKTBN) Sepang, Bandar Baru Salak Tinggi, 43900 Sepang, Selangor, Malaysia 2Solar Energy Research Institute (SERI), Universiti Kebangsaan Malaysia,43600 Bangi, Selangor, MALAYSIA 3Department of Materials & Life Sciences, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japan 4Department of Chemical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia 5Battery Technology Research Group (UKMBATT), Polymer Research Centre (PORCE), Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MALAYSIA 6Product Stewardship and Toxicology, Group Health, Safety and Environment (GHSE), Petroliam Nasional Berhad (PETRONAS), 50088, Kuala Lumpur, Malaysia Corresponding email: [email protected]$EVWUDFWfflTetraalkylammonium salts (TAS) are multifunctional materials known for enhancing the efficiencyand stability of perovskite solar cells (PSC). As the demand for greener technologies grows, producing TAS from renewable sources is a crucial step toward sustainable energy innovation. However, there is limited research on optimizing the synthesis of high-purity TAS precursors, particularly for use in PSC applications. This study introduces a sustainable approach to synthesizing biobased fatty amide (FA) via an amidation reaction between fatty acids and tris(3-aminopropyl)amine. The resulting FA serves as a precursor for TAS iodide, which is then used in PSC. Key synthesis parameters were optimized, and the physicochemical properties of both the FA and TAS were characterized. Spectroscopic analyses confirmed 100% trisubstitution in FA and increase in TAS purity via liquid-liquid extraction purification and revealed the TAS showed properties similar to ionic liquid crystals, favourable for modern energy applications. Incorporation of TAS into perovskite reduced the energy bandgap and improved crystallinity. The PSC device achieved an optimal power conversion efficiency of 3.9%. This study highlights the promising potential of high-purity biobased FA as a sustainable precursor for TAS production, supporting the advancement of high-efficiency and environmentally friendly PSC technologies aligned with the goals of TVET innovation and energy transition industries..H\\ZRUGVffl Ammonium Salt; Bio-based; Perovskite; Precursor; Solar Cell; TASxMAI1-xPbI3,1752'8&7,21The rapid advancement of perovskite solar cells (PSCs) in recent years has been a focal point of research in the field of renewable energy, primarily due to their remarkable power conversion efficiency (PCE) and potential to surpass traditional silicon-based solar cells [1ñ3]. Among the materials contributing to this progress, tetraalkylammonium salts (TAS) have proven to be multipurpose, playing critical roles in various applications such as electrolytes [4], synthesis reactants [5,6], and interfacial layers [7,8]. However, the shift from petrochemical to bio-based TAS has gained momentum due to the latterís reduced environmental impact, widespread availability, cost-effectiveness, and biodegradable nature [9]. Bio-based TAS have increasingly replaced their petrochemical counterparts in PSCs over the past few decades. This transition is driven by the need for sustainable and eco-friendly alternatives in the solar cell industry. The production of high-purity TAS from biobased fatty amide (FA) precursors has become essential for optimizing the performance and stability of PSCs. Consequently, the purification process of bio-based FA is of significant industrial interest, aiming to produce TAS with enhanced properties. Bio-based TAS have gained widespread use in PSCs over the past few decades, displacing their petrochemical counterparts due to their reduced environmental impact, widespread availability, cost-effectiveness, and biodegradable nature, making the purification of bio-based FA to serve as a precursor .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7288
important in industrial applications to produce higherpurity TAS. This study aims to characterize the physicochemical and thermal properties of TAS and to determine the performance efficiency of bio-based TAS at different mol fraction TASxMAI1-xPbI3 in PSC applications. By addressing these objectives, this study aims to enhance the understanding and application of bio-based TAS in PSC, paving the way for more sustainable and efficient solar energy solutions. 0$7(5,$/6$1'0(7+2'60DWHULDOV IRU )$ DQG 7$6: Lauric acid (purity !97.0%) was purchased from Nacalai Tesque, Japan. Tris(3-aminopropyl) amine (purity > 97.0%) and iodopropane (purity > 98.0%) were obtained from Tokyo Chemical Industries (TCI), Japan. Potassium acetate (purity ! 97.0%) was obtained from Sigma-Aldrich Co, Italy. Toluene (purity ! 99.0%), methanol (purity ! 99.0%), ethanol (purity ! 99.0%), diethyl ether (purity ! 99.0%) and chloroform (CHCl3) (purity ! 99.0%) used were manufactured by Chemiz, Malaysia. 0DWHULDOVIRU3HURYVNLWH6RODU&HOO: Lead (II) iodide(purity!99.0%), 4-tert-butiylpiridinayridine(purity!96%),Titanium diisopropoxide bis(acetylacetonate) (75 wt%), Lithium bis[(trifluoromethyl)sulfonyl] imide salt (purity!99%), 2,20,7,70-tetrakis-(N, Ndipmethoxyphenylaminedipmetoksifenilamine)-9,90-spirobifluorine (spiro-OMETAD) (purity!99%), N,Ndimethylformamide (purity!99.8%), acetonitrile (purity!99.8%), and 1-butanol (99.5%) were purchased Sigma Aldrich/Merck. Methylammonium iodide (MAI) and TiO2 paste (30 NR-D) were obtained from Greatcell Solar Materials. Absolute ethanol (purity! 99.95%), isopropanol (purity! 99.5%), chlorobenzene, (purity!99.0%) were purchased from R&M Chemicals. Fluorine-doped tin oxide (FTO, TEC15, 15 U/sq) was obtained from Pilkington. 6\\QWKHVLV)$DQG7$6The amidation reaction between a lauric acid (CH\"(CH#)$'COOH and tris(3-aminopropyl) amine (TPA) was conducted at 110°C with continuous stirring under a reflux condition according to the patent filed by M.S. Suíait et al. [10]. A multiple liquid-liquid extraction (LLE) process was then employed to enhance the purity of biobased FA by tunning the selection of solvent based on its relative polarity of the reactant and products. The resulting mixture was washed with two solvent systems according to previous studies by Mustafa et al [11]. TAS was then synthesized by a quaternization reaction between FA that has been purified with LLE and reacted with excess iodopropane (also acted as a solvent), which was added dropwise into the reaction mixture with agitation by a stirrer for 60 min at 0 *C. The reaction was carried out at 65 °C for 24 hours with magnetic stirring. Diethyl ether was added dropwise into the reaction mixture after the temperature was slowed down. The final product was left for about two days before being filtered to obtain purified TAS [4].3HURYVNLWH6RODU&HOO)DEULFDWLRQThe FTO-coated glass substrate (25 mm length × 25 mm) was cleaned using a sequential sonication method insolutions of 2% Hellmanex aqueous, deionized water,acetone, and 2-propanol for 30 minutes each. The cleaningprocedure was followed by UV-ozone treatment to removeorganic residues and enhance dewettability [12,13]. 200 µLof titanium isopropoxide bis(acetylacetonate) precursorsolution (TiAAc) in 16 ml ethanol was first sonicated in anultrasonic machine for 10 minutes. A solid TiO2 layer (cTiO2) was then deposited on each substrate via spraypyrolysis at 300 ºC. Each substrate was subsequentlyannealed at 450 ºC for 30 minutes with a box furnace(KBF314N, Koyo Thermo Systems Co., Ltd.) and allowedto cool to room temperature. The substrates were cleanedwith a UV ozone cleaner, and the process continued withthe deposition of a mesoporous TiO2 layer (m-TiO2) byspin-coating a 75 wt.% ethanol solution of TiO2 composite(18 NR) at 500 rpm for 10 seconds, followed by 3000 rpmfor 30 s. The m-TiO2 layer was prepared beforehand, wherethe m-TiO2 powder from Greatcell Solar (18 NR-T), whichis orange-yellow in color, was dissolved in ethanol at aratio of 1:9 and sonicated for 10 minutes first. The TiO2solution was used during the initial slow-speed spin. Afterthe spin-coating, the obtained film was annealed at 500 ºCfor 40 minutes with a box furnace (Hamaguchi et al. 2017).230.9 mg of PbI2 and 79.5 mg of MAI were dissolved in asolvent mixture containing 450 µL of dimethylformamide(DMF) and 50 µL of dimethyl sulfoxide (DMSO) to obtaina precursor solution for preparing the MAPbI3 solution.The solution was heated to 70 ºC with magnetic stirring for30 minutes. The MAPbI3 solution was then stirred beforebeing spin-coated with 100 µL onto the mesoporous TiO2layer at 2000 rpm for 7 seconds to prepare the perovskitefilm. The perovskite film was then spin-coated with 300µL of chlorobenzene solution at 6000 rpm for 30 secondsbefore the glass substrate was heated to 70 ºC for 15minutes. This step was repeated by replacing the MAImaterial with the synthesized TAS salt ligand from theprevious step, where the precursor mol fraction was set toTASxMAI1-xPbI3 where x = 0.0.01, 0.03, and 0.05. A 70.0µL solution containing a mixture of 170 mg lithiumbis(trifluoromethanesulfonyl)imide (Li-TFSI) in 1 mLacetonitrile (ACN) along with 13.4 µL of a solutioncontaining 300 mg tris(2-(1H-pyrazol-1-yl)pyridine)cobalt(III) tri(hexafluorophosphate) (FK 102Co(III)PF6 salt) in 1 mL acetonitrile (ACN), and 800 µL ofa solution containing 68 mg spiro-OMeTAD and 27 µL 4-tert-butylpyridine dissolved in 1 mL chlorobenzene with an.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7289
ACN solution. All these solution mixtures were left to dissolve with magnetic stirring overnight. Subsequently, 100 µL of the spiro-OMETAD solution was spin-coated at 3000 rpm for 30 seconds onto the perovskite layer followed by overnight aging. Finally, Au was vapordeposited over top as a cathode[12, 13].&KDUDFWHUL]DWLRQ Physicochemical CharacterizationThe FTIR spectra were scanned between 4000 ñ 400 cm-1 with the scanning resolution of 2 cm-1 at roomtemperature. Liquid chromatography time-of-flightelectrospray ionization (LC-TOF-ESI) mass spectroscopyanalyses were done using model MicroTOF-QIII BrukerDaltanoic. The ionization technique used was electrosprayionization (ESI) with positive ion polarity. The massspectroscopy conditions were as follows: capillary voltages= 4500 V, nebulizer gas pressure = 1.2 bar, and drying gasflow = 8.0 L/min at 200 °C. The mass range was scannedbetween 50 to 1000 m/z. Analysis of nuclear magneticresonance (NMR) spectroscopy were carried out using aBruker Fourier Transform NMR (FT-NMR) modelAdvance III 600 MHz equipped with a cryoprobe and atriple resonance channel. Thermal characterization wasperformed using TGA (Hitachi High-Technologies) andDSC (Model: DSC Q20).Device CharacterizationXRD Powder diffraction patterns were collected using a Bruker Advance D8 X-ray diffractometer with a Cu K+ source. Measurements were taken from 2- values of 5° to 80°. UV/vis spectroscopy-thin film optical transmission and reflectance measurements were performed on a PerkinElmer Lambda 750S UV-Vis spectrometer, from 900 nm to 300 nm. Absorption was calculated as + = log(1/RT). The current-voltage (J-V) characterization was conducted using a Keithley 2400 source meter and a Newport solar simulator equipped with a 300 W Xenon arc lamp to evaluate the performance of each PSC device with varying molar fractions of TASxMAI1-xPbI3. The solar irradiance was set to 1000 W/m², which serves as the standard value for testing solar cells, verified using a DAYSTAR solar meter. The J-V scan rate was recorded at 0.1 V/s to investigate hysteresis behavior. SEM and AFM images Top-view SEM images were taken on a JEOL SEM 6480LV, at an acceleration voltage of 5 kV. 5(68/76 $1'',6&866,2163K\\VLFRFKHPLFDO3URSHUWLHVThe effect of the quaternization reaction in the production of TAS was studied through FTIR analysis, with all observed wavenumber signal summarized in (7DEOHand)LJXUH ). The FTIR spectrum of TAS did not showsignificant changes in the wavenumber range, as tertiary or quaternary amines are theoretically undetectable by (reference). However, the quaternization reaction significantly influenced the intensity of N-H stretching, C=O stretching, and N-H bending peaks, suggesting structural transformation of FA into TAS (()LJXUH D DQG E ). The N-H stretching band shifted from 3280cm;¹ in FA to 3300 cm;¹ in TAS. A slight shift was also observed in the N-H bending peak at 1560 cm;¹ (FA) which shifted to 1557 cm;¹ in TAS with reduced peak intensity, indicating that the quaternization occurred at the N-H functional group, consistent with previous findings by(reference). Meanwhile, the C=O stretching spectra for FAand TAS, occurring around ~1640 cm;¹, indicated that thecarbonyl group remained stable and was minimallyaffected by the quaternization reaction (reference). Theobservations suggest that alkyl chain substitution in FAoccurred at central tertiary amine nitrogen. This conclusionis further supported by mass spectroscopy and NMRanalysis, which will be discussed in the subsequentsubsection.7DEOH The wavenumber signals of the FTIR spectrum forFA and TAS)XQFWLRQDOJURXS:DYHQXPEHU FP /DXULFDFLG & 73$ )$ 7$6N-H stretch - 3357,3282(2 peaks -primaryamine)3280(1 peaksecondary amide)3300N-H bend - 1599 1560 1557C-N - 1380 1368C-H 1472 1466 1471C-H 3042 - 3100C=O stretch 1698 - 1640 (secondaryamide)1641CH2 1466 1469 1467CH3 1373 - 1368C-O stretch 1330-1222 - --O-H 3034 - -C-H 3288 - -Subsequently, the characterization of TAS salts was conducted using the Fast Atom Bombardment (FAB)-MS method at the Sophia University Laboratory in Japan. In addition to characterizing the m/z of cations, this method can also characterize and confirm the presence of anions in the tested compounds [14]. Therefore, the characterization of iodide anions through the quaternization reaction of fatty amides and iodopropane into the TAS salt structure can be confirmed. )LJXUH D and E show the TASspectra. The same peaks at m/z 240 and 735 also appear, representing the TAS structure with 777, as shown by the molecular ion peak, M+. The molecular weight (Mw).219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7290
of the compound calculated with the possible substitution of propyl chains into the FA. )LJXUH FTIR Spectrum of TAS synthesized from FAwith LLE.The characterization of iodide anions is further confirmed by the presence of the m/z 126 peak in )LJXUH E The presence of this peak with a relative abundancepercentage of 100% proves that the addition of iodide anions into the TAS salt structure through the quaternization reaction between fatty amide compounds and iodo-propane was successful. Through the characterization analysis of this TAS salt compound, it can be concluded that the purity of the TAS structure can be significantly increased with LLE purification, where the relative abundance percentage of TAS increased to 60% compared to previous studies that also produced TAS using FA without LLE purification [11]. However, with the presence of the fatty amide peak at m/z 735 still visible, future studies are recommended to explore purification methods at the TAS synthesis stage to improve its purity. Additionally, the FAB-MS method has proven to be highly effective for analyzing non-volatile and thermally unstable polar compounds. The ionization products [M+H]+and [M-H]-are generated in pairs in FAB, facilitating the analysis of both positive and negative ion mass spectrometry. Both ESI and FAB mass spectrometry methods have high sensitivity and use small sample amounts of around 10-30 mg for analysis [14].)LJXUH Mass spectrum of TAS cation and anionpeaks indicating the presence of iodide anions in the TAS using Fast Atom Bombardment (FAB)-MS.The peak determination in the 13C-NMR spectrum of TAS salts in Figure 3(a) is as follows: < = 14.27 (C1,C17), < = 22.85 (C2), < = 26.07 (C3), < = 27.08 (C4), < = 29.59-29.84 (C5-C10), < = 32.09 (C11), < = 36.93 (C12), < = 37.93 (C13,C16), < = 51.61 (C14, C15), dan < = 173.79 (C=O). The presence peak at the following chemical shifts: < = 14.27 (C17), ), < = 37.93 (C16), and < = 51.61 (C15) confirms the substitution of the propyl chain from iodopropane into the FA structure indicates the addition of a propyl chain to the central nitrogen atom, resulting in a deshielding effect. The chemical shift values of other peaks correspond to the formation of TAS with varying chain lengths, consistent with findings reported by Alrubaie et al 2022. The optimization of FA notably enhances the purity of TAS, as evidence by the clearer NMR spectra of the material, which show the elimination of residual unreacted reactant peaks when compare to ealier studies by Jumaat et al. [4]. Subsequently, 2D-NMR spectroscopic characterization of the TAS structure, including correlation spectroscopy (COSY), heteronuclear multiple-bond correlation (HMBC), and heteronuclear single quantumThe thermal behaviour characteristics of FA and TAS were investigated through Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC) .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7291