Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026139PEMPROSESAN BAHASA TABII – TSI 3373NATURAL LANGUAGE PROCESSING – TSI 33733 Credit HoursPrerequisite: NoneCourse SynopsisNatural Language Processing (NLP) covers any kind of computer manipulation of natural language. NLP involves “understanding” complete human utterances. Technologies based on NLP are becoming increasingly widespread. For example, phones and handheld computers support predictive text and handwriting recognition; web search engines give access to information in unstructured text; machine translation etc. By providing more natural human-machine interfaces, and more sophisticated access to stored information, language processing has come to play a central role. Therefore, this syllabus will introduce text corpora, lexical resources, processing raw text, word tagging, text classification, information extraction, sentence structures with context free grammar and sentence meaning.Course OutcomesAt the end of this course, students are able to:1. Describe the concepts and technique in natural language processing for computer manipulation of natural language. 2. Construct technique for the implementation of natural language understanding in computer systems. 3. Practice the NLP techniques to related applications. References1. Hobson Lane, Cole Howard, Hannes Max Hapke. 2019. Natural Language Processing in Action: Understanding, analysing, and generating text with Python. Manning Publications Co.2. Sowmya Vajjala, Bodhisattawa Majumder, Anuj Gupta, Harshit Surana. 2020. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems. O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA.3. Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.4. Christopher D. Manning and Hindrich Shütze. 1999. Foundations of Statistical Natural Language Processing 1st Edition. MIT Press Ltd. Cambridge, United States.5. Dan Jurafsky and James H. Martin. 2020. Speech and Language Processing 3rd Edition. Pearson Education,Inc.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026140INTERNET BENDA – TSI 3363INTERNET OF THINGS – TSI 33633 Credit HoursPrerequisite: NoneCourse SynopsisThe objective of the course is to expose the students to the concept, task, technique and algorithm in of Internet of Things. Students will also be exposed to apply of Internet of Things techniques to a particular applications such as for smart and remote monitoring system. Internet of Things (IoT) is presently a hot technology worldwide. Government, academia, and industry are involved in different aspects of research, implementation, and business with IoT. IoT cuts across different application domain verticals ranging from civilian to defence sectors. These domains include agriculture, space, healthcare, manufacturing, construction, water, and mining, which are presently transitioning their legacy infrastructure to support IoT. IoT-based applications such as innovative shopping system, infrastructure management in both urban and rural areas, remote health monitoring and emergency notification systems, and transportation systems, are gradually relying on IoT based systems. Therefore, it is very important to learn the fundamentals of this emerging technology.Course OutcomesAt the end of this course, students are able to:1. Describe concepts and techniques of Smart Objects and IoT Architectures.2. Discover about various IOT-related programming and protocols.3. Develop simple IoT Systems using Arduino and Raspberry Pi.References1. Adeel Javed. (2016). Building Arduino Projects for the Internet of Things, Apress .2. Adrian McEwen, Hakim Cassimally. (2013). Designing the Internet of Things. Wiley3. Anuradha, J, Tripathy, B.K., I. (2018). Internet of things (IoT) : technologies, applications, challenges and solutions. CRC Presss: Taylor & Francis4. Dimitrios Serpanos, Marilyn Wolf, (2018). Internet-of-Things (IoT) Systems: Architectures, Algorithms, Methodologies.Springer International Publishing.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026141MULTIMEDIA - TSI 3713MULTIMEDIA - TSI 37133 Credit HoursPrerequisite: NoneCourse SynopsisStudents need to master this course well because it Identifies all multimedia elements and distinguishes the appropriate audio and video techniques in a multimedia project development. The students will also apply the proper techniques of multimedia knowledge in developing a multimedia project. Students from other specialisations will also find this course attractive because multimedia technology has many applications in various fields such as, advertising, education, marketing, industrial and social.Course OutcomesAt the end of this course, students are able to:1. Define concepts and techniques of multimedia computing and its importance.2. Gain sufficient knowledge on the multimedia elements and its application.3. Apply the proper techniques of multimedia knowledge in developing a multimedia project. References1. Atul P. Godse, Dr. Deepali A. Godse. (2021). Computer Graphics and Multimedia. Technical Publications.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026142REALITI MAYA - TSI 3733VIRTUAL REALITY - TSI 37333 Credit HoursPrerequisite: NoneCourse SynopsisThe course teaches the fundamentals of Virtual Reality (VR) and provides laboratory experiences where students learn how to develop immersive, interactive and animated 3D computer models. Authoring tools like 3D Studio Max, VirTools and VRML will be introduced and allowed for creating unique applications in the arts, engineering, humanities, medicine, science or any other areas. The course emphasises on cross-discipline collaboration and teamwork in group projects. Each team will develop a complete virtual reality application in the area of interest.Course OutcomesAt the end of this course, students are able to:1. Explain Virtual Reality Technology and the implementation in various fields.2. Describe the functionality and Human Senses that are related to VR Technology.3. Build a VR application system in diverse fields.References1. LaValle, S.M. (2016). Virtual Reality. Boston: Cambridge University Press.2. Blascovich, J. & Bailenson, J. (2012). Infinite Reality: The Hidden Blueprint of our Virtual Lives. New York: William Morrow Paperbacks. 3. Jerald, J. (2015). The VR Book: Human-Centered Design for Virtual Reality. New York: Morgan & Claypool Publishers, ACM Books.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026143PENYELIDIKAN OPERASI – TSI 3743OPERATIONAL RESEARCH - TSI 37433 Credit HoursPrerequisite: NoneCourse SynopsisThis course is designed to expose students with modelling, solution and analysis of such optimisation problems that are found in various industries and application areas. The use of optimisation is common in computer science especially in artificial intelligence and computer security. Examples of applications are widely encountered in transportation and logistics, manufacturing environments, service operations, product design and development and so forth. Topics include linear programming, transportation model, network model, project management, and analytic hierarchy process.Course OutcomesAt the end of this course, students are able to:1. Explain basic concepts of objectives, decision variables, constraints correctly, transportation, assignment, networks, queuing models and simulation. 2. Demonstrates knowledge with the basic notions and techniques for algorithm and basic operations research. 3. Practice simple operational research problems using linear programming, network model, transportation model and simulation model. References1. Knight, V. & Palmer, G. (2022). Applied Mathematics with Open-Source Software: Operational Research Problems with Python and R (Chapman & Hall/CRC Series in Operations Research) 1st Edition. Chapman and Hall/CRC.2. Anderson, S. and Williams, M. (2018). An Introduction to Management Science, 15th Edition. Washington: Cengage Learning. \"
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026144E-DAGANG – TSI 3383E-COMMERCE – TSI 33833 Credit HoursPrerequisite: Course SynopsisThis course focuses on e-commerce principles from a business perspective, providing an overview of business and technology topics, business models, virtual chains, social innovation and marketing strategies. In addition, some major issues associated with e-commerce include security, privacy, intellectual property rights, authentication, encryption, acceptable use policies, and legal liabilities. Topics covered include E-business Models, E-business Infrastructure, Selling and Marketing on the Web, Web Server Hardware and Software, B2C and B2B strategies, Virtual Communities, Web Portals, E-commerce Software, Payment Systems, Social Media, Security and User Experience.Course OutcomesAt the end of this course, students are able to:1. Determining electronic commerce and the stakeholders and their capabilities and limitations in the strategic convergence of technology and business.2. Organise components, systems and/or processes to meet required specifications for a web presence.3. Develop awareness of ethical, social and legal aspects of e-commerce and propose features of existing e-commerce businesses, future directions or innovations for specific businesses.References1. Laudon, K.C,& Traver, C.G., (2020), E-Commerce 2020–2021: Business, Technology and Society, Global Edition, 16th edition, Pearson InternationalEC-Council. (2016).2. Hammersley, I. & Hammersley, M. (2018), Ultimate Guide To E-commerce Growth. Smartebusiness Ltd.3. Norfolk, M. & Holden, (2011) G. Starting an Online Business For Dummies, John Wiley & Sons Australia Ltd
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026145KESELAMATAN RANGKAIAN WAYARLES - TSS 3343WIRELESS NETWORK SECURITY - TSS 33433 Credit HoursPrerequisite: NoneCourse SynopsisSecuring wireless networks is extremely important and challenging due to the nature of wireless connectivity. Unprotected wireless networks are vulnerable to several security attacks including eavesdropping and jamming that have no counterpart in wired networks. The topics that will be discussed are wireless network security fundamentals, types of wireless network security technology, wireless standards, enhanced security for wireless LANs and WANs in the enterprise, handling wireless private information, wireless network security – design issues, cost justification and consideration, standards design issues, implementation plan development, wireless network security planning techniques, testing techniques, installation and deployment and management of wireless network security.Course OutcomesAt the end of this course, students are able to:1. Show the concepts in wireless networking, protocols, and standards. 2. Adapt about threats faced by wireless networks. 3. Demonstrate the concepts in planning, designing, and implementing of a secure network. References1. Yi Q., Feng Y.; Hsiao-Hwa C. (2022). Security in Wireless Communication Networks: WileyIEEE Press2. Wolfgang O. (2021). Wireless Network Security Second Edition: Routledge, Taylor & Francis. 3. Meyers, R. (2019). Wireless Network Security: Introduction and Explanation of Cybersecurity and Hacking Technology for Wireless System, Kali Linux Tools and Other: Independently
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026146ANALITIK DAN PEMBANGUNAN DATA RAYA – TSV 3343BIG DATA ANALYTICS AND DEVELOPMENT – TSV 33433 Credit HoursPrerequisite: NoneCourse SynopsisThis course aims to provide an overview of advanced machine learning, data mining and statistical techniques that arise in data analytic applications. In this course, students will learn and practice data analytic techniques, including parallel algorithms, online algorithm, locality sensitive hashing, topic modeling, structure learning, time-series analysis, and data development techniques. One or more warfare applications associated with each technique will also be discussed and applied.Course OutcomesAt the end of this course, students are able to:1. Define basic theory and concepts of big data analytics and data science process.2. Apply data science process and machine learning algorithms to solve real world problems (e.g., Linear regression, classification and clustering). 3. Build data analytics model to solve real world problems (e.g., By applying machine learning techniques and using data analytics tool). References1. Agrawal, P., Gupta, C., et. al (2022). Machine Learning and Data Science: Fundamentals and Applications 1st Edition. Wiley-Scrivener.2. Burk, S. & Miner, G.D. (2022). It’s All Analytics!: The Foundations of AI, Big Data, and Data Science Landscape for Professionals in Healthcare, Business, and Government 1st Edition. Productivity Press.3. Li, K.C., Jiang, H., Yang, L.T. & Cuzzocrea, A. (2015). Big Data: Algorithms, Analytics, and Applications. Boca Raton: CRC Press.4. Loshin, D. (2013). Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph. New York: Elsevier Science.5. Services, E.M.C.E. (2015). Data Science and Big Data Analytics: Discovering, Analysing, Visualising and Presenting Data. Indianapolis: John Wiley & Sons Inc.\"
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026147PENGENALAN KEPADA ANALITIK DATA – TSV 3313INTRODUCTION TO DATA ANALYTICS – TSV 33133 Credit HoursPrerequisite: NoneCourse SynopsisThis course introduces the basics of data analytics and modeling for handling of massive databases. The course covers concepts of data analysis for big data analytics, and introduces the students to some practicalities of map-reduce while adopting the big data management life cycle. In this course, students will be taught on how to develop appropriate algorithms for modeling and visualising these high dimensional data sets and gain insights into these algorithms from theoretical and empirical perspectives of analysing massive datasets. The course emphasises that business analytics is not a theoretical discipline: these techniques are only interesting and important to the extent that they can be used to provide real insights and improve the speed, reliability, and quality of decisions. The concepts learned in this class should help students identify opportunities in which data analytics can be used to improve organisation performance and support important decisions.Course OutcomesAt the end of this course, students are able to:1. Explain concepts of knowledge discovery process, data pre-processing techniques. 2. Conduct data exploration and data analytics techniques. 3. Build data analytics model and interpret result. References:1. Nelli, F. (2018). Python data analytics. Apress Media, California. 2. Navlani, A., Fandango, A., & Idris, I. (2021). Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python. Packt Publishing Ltd.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026148FORENSIK DATA DAN MEDIA DIGITAL – TSF 3333DATA AND DIGITAL MEDIA FORENSICS – TSF 33333 Credit HoursPrerequisite: NoneCourse SynopsisThe area of digital media forensics is not just the art of finding deleted or hidden data but it is also the understanding of the underlying technologies behind the various tools used and the ability to present scientifically valid information. In this course, students will deal with the collection, preservation and analysis of digital media such that the evidence can be successfully presented in a court of law.Course OutcomesAt the end of this course, students are able to:1. Distinguish concepts and methods to acquire digital evidence from various digital media. 2. Practice the underlying technologies behind the various tools used in digital media analysis and forensics. 3. Explain digital evidence using scientifically derived and proven methods that can be used to facilitate or further the reconstruction of events in an investigation. References1. Moreb, M. (2022). Practical Forensic Analysis of Artifacts on iOS and Android Devices: Investigating Complex Mobile Devices. Apress.2. Easttom, C. (2021). An In-Depth Guide to Mobile Device Forensics 1st Edition. CRC Press.3. Bommisetty, S., Tamma, R., Skulkin, O. & Mahalik, H. (2018). Practical Mobile Forensics. Third Edition. Birmingham: Packt Publishing.4. EC-Council. (2016). Computer Forensics: Investigating Data and Image Files (CHFI). Boston: Cengage Learning.5. EC-Council. (2016). Computer Forensics: Investigating File and Operating Systems, Wireless Networks, and Storage (CHFI). Boston: Cengage Learning.\"
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026149PENGINTIPAN KETENTERAAN DAN INDUSTRI – TSF 3733MILITARY AND INDUSTRY ESPIONAGE – TSF 37333 Credit HoursPrerequisite: NoneCourse SynopsisIn this course, students will learn to define and describe the espionage. This module examines the motivations for military and industrial espionage and the various methods of attack on the physical security of an organisation, its electronic infrastructures and its staff and suppliers. Students will learn to analyse and mitigate potential attacks through military and industrial espionage, and will carry out risk management processes in military and industrial espionage.Course Learning OutcomesAt the end of this course, students are able to:1. Explain defense and countermeasures for potential attacks in the military and industrial espionage cases. 2. Assess the concepts, types and characters of military and industrial espionage. 3. Build basic espionage behaviours and characteristics for analysing military and industrial espionage. References1. Pehlivan, O. K. (2018). Confronting Cyberespionage Under International Law. Routledge. 2. Clancy, T. & Greaney, M. (2012). Threat Vector. New York: Penguin Publishing Group.3. Stoll, C. (2012). Cuckoo’s Egg. New York: Knopf Doubleday Publishing Group.4. Brown, A. (2011). The Grey Line: Modern Corporate Espionage and Counterintelligence. Columbus: Amur Strategic Research Group.5. Winkler, I. (2005). Spies Among Us: How to Stop the Spies, Terrorists, Hackers, and Criminals You Don’t Even Know You Encounter Every Day. Indianapolis: Wiley.6. Carr, J. (2011). Inside Cyber Warfare: Mapping the Cyber Underworld. Sebastopol: O’Reilly Media.\"
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026150SINOPSIS KURSUS PENGKHUSUSANPROGRAM IJAZAH SARJANA MUDA SAINS KOMPUTER(KESELAMATAN SISTEM KOMPUTER) (ZC27)KRIPTOGRAFI TSS 3313CRYPTOGRAPHY - TSS 33133 Credit HoursPrerequisite: NoneCourse SynopsisThis is a course in modern cryptography emphasising formal definitions and proofs of security. Core topics include private- and public-key schemes for encryption and message authentication, cryptographic hash functions, and authenticated encryption schemes. Additionally, the course includes some analyses of the Data Encryption Standard (DES) block cipher, Rivest Cipher 4 (RC4) stream cipher, and real world security protocol such as Secure Sockets Layer (SSL).Course OutcomesAt the end of this course, students are able to:1. Acquire concepts of cryptography. 2. Investigate cryptographic techniques and principles such as symmetric encryption,asymmetric encryption, key management, hashing and message digest.3. Integrate a secure web server using Hypertext Transfer Protocol Secure (HTTPS) and Secure Socket Layer (SSL). References1. Cryptography and Network Security - Principles and Practice, 8th Edition, William Stallings Pearson Education,134444280 (15-Sept-20)2. Introduction to Modern Cryptography: Third Edition, Jonathan Katz, Yehuda Lindell, Chapman and Hall,815354363 (21-Dec-20) 3. MYSEAL: MySEAL, National Trusted Cryptographic Algorithm List (Senarai Algoritma Kriptografi Terpercaya Negara) retrieved from https://mykripto.cybersecurity.my/index.php/services/myseal
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026151FORENSIK DIGITAL - TSS 3323DIGITAL FORENSICS - TSS 33233 Credit HoursPrerequisite: NoneCourse SynopsisThis course takes a detailed, hands-on approach to the investigation of incidents, detection hacking attacks and extracting evidence to report the crime and conduct audits to prevent future attacks in which computers or computer technology play a significant role. Students completing this course will be familiar with the core computer science theory and practical skills necessary to perform rudimentary computer forensic investigations: discovering data, recovering deleted data or damaged file information, understand the role of technology in investigating computer-based crime: tracing the originator of defamatory e-mails to recover signs of fraud, and be prepared to deal with investigative bodies at elementary level to prosecute the necessary evidence in the court of law. This course will incorporate significant components of industrial and technical training, which includes certifications from EC Council. Course OutcomesAt the end of this course, students are able to:1. Understand the concepts and techniques of digital forensics and its importance.2. Gain sufficient knowledge on the legal matters pertaining digital forensics. Conduct a proper digital forensic investigation. References1. Bunting, S. (2007). EnCase Computer Forensics: The Official EnCE - EnCase Certified Examiner Study Guide. New York: John Wiley & Sons.2. Casey, E. (2009). Handbook of Digital Forensics and Investigation. London: Elsevier Inc.3. Cowen, D. (2012). Computer Forensics: A Beginner’s Guide. New York: McGraw-Hill Osborne.4. Vacca, J.C. (2005). Computer Forensics: Computer Crime Scene Investigation. Second Edition. New Jersey: Charles River Media.5. Wiles, J., Cardwell, K. & Reyes, A. (2007). The Best Damn Cybercrime and Digital Forensics Book Period. Maryland Heights: Syngress.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026152KESELAMATAN RANGKAIAN WAYARLES - TSS 3343WIRELESS NETWORK SECURITY - TSS 33433 Credit HoursPrerequisite: NoneCourse SynopsisSecuring wireless networks is extremely important and challenging due to the nature of wireless connectivity. Unprotected wireless networks are vulnerable to several security attacks including eavesdropping and jamming that have no counterpart in wired networks. The topics that will be discussed are wireless network security fundamentals, types of wireless network security technology, wireless standards, enhanced security for wireless LANs and WANs in the enterprise, handling wireless private information, wireless network security – design issues, cost justification and consideration, standards design issues, implementation plan development, wireless network security planning techniques, testing techniques, installation and deployment and management of wireless network security.Course OutcomesAt the end of this course, students are able to:1. Distinguish wireless networking concepts, protocols, standards, and potential threats.2. Practice the underlying concepts in planning, designing and implementing of a secure network.3. Explain scientifically through the auditing of wireless network security using wireless network analysis tools.References1. Buttyan, L. & Hubaux, J.P. (2007). Security and Cooperation in Wireless Networks. Boston: Cambridge University Press.2. Chache, J., Wright, J. & Liu, V. (2010). Hacking Exposed Wireless: Wireless Security Secrets & Solutions. Second Edition. New York: McGraw-Hill Osborne Media.3. Coleman, D.D., Westcott, D.A., Harkins, B.E. & Jackman, S.M. (2010). CWSP Certified Wireless Security Professional Official Study Guide. New York: Sybex4. Vacca, J.R. (2006). Guide to Wireless Network Security. Berlin: Springer. 5. Wrightson, T. (2012). Wireless Network Security A Beginner's Guide. New York: McGrawHill Osborne Media.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026153PENGGODAM BERETIKA - TSS 3353ETHICAL HACKER - TSS 33533 Credit HoursPrerequisite: NoneCourse SynopsisThe course introduces the knowledge on how hackers attacks computers and networks and how to protect systems from hackers. Students will learn legal restrictions and ethical guidelines, and will be required to obey them. Students will perform hands-on labs such as port scanning, footprinting, sniffing and other techniques used by computer hackers.Course OutcomesAt the end of this course, students are able to:1. Categorise vulnerabilities in computer networks and systems. 2. Explain hacking techniques used to breach and exploit the computers and networks.. 3. Report countermeasures to mitigate the security threats.References1. Gregg, Michael. (2017). Certified Ethical Hacker (CEH) Version 9 Cert Guide (Certification Guide). 2nd Edition. Pearson IT Certification, Indiana.2. Oriyano, Sean-Philip . (2016). CEH v9: Certified Ethical Hacker Version 9 Study Guide. 3rd Edition. Sybex. New Jersey.3. Rahalkar, Sagar Ajay. (2016). Certified Ethical Hacker (CEH) Foundation Guide. Apress. New York. 4. Regalado, D., Harris, S., Harper, A., Eagle, C., Ness, J., Spasojevic, B., Linn, R and Sims, S. (2015). Gray Hat Hacking: The Ethical Hacker’s Handbook. 4th Edition. McGrawHill/Osborne. New York.5. Walker, Matt. (2016). CEH Certified Ethical Hacker All-in-One Exam Guide. 3rd Edition. McGraw-Hill Education. New York.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026154SISTEM PENGESAN PENCEROBOHAN RANGKAIAN - TSS 3743NETWORK INTRUSION DETECTION SYSTEM - TSS 37433 Credit HoursPrerequisite: TSS 3313 CryptographyTSS 334 Wireless Network SecurityTSS 3353 Ethical HackerCourse SynopsisIn this course, students gain knowledge of how attackers break into networks, and how an Intrusion Detection System (IDS) can play a key role in detecting and responding to these events. Students will learn how to configure, deploy and tune IDS to identify exploits occurring in organisations. This course also teaches how to recognise the various stages of attacks and intrusions.Course OutcomesAt the end of this course, students are able to:1. Understand the concept and components of Network Intrusion Detection System.. 2. Demonstrates the behaviour of network attacks.3. Design a working and suitable Network Intrusion Detection System.References1. Caswell, B., Beale, J. & Baker, A. (2007). Snort IDS and IPS Toolkit. Maryland Heights: Syngress.2. Fearnow, M., Northcutt, S., Frederick, K. & Cooper, M. (2001). Intrusion Signatures and Analysis. Carmel: Sams Publishing.3. Greg, C. & Cox, K.J. (2004). Managing Security with Snort and IDS Tools. San Francisco: O'Reilly Media, Inc.4. Northcutt, S. & Novak, J. (2002). Network Intrusion Detection. Third Edition. Carmel: Sams Publishing. 5. Sanders, C. (2011). Practical Packet Analysis. Second Edition. San Francisco: No Starch Press.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026155MULTIMEDIA - TSI 3713MULTIMEDIA - TSI 37133 Credit HoursPrerequisite: NoneCourse SynopsisStudents need to master this course well because it Identifies all multimedia elements and distinguishes the appropriate audio and video techniques in a multimedia project development. The students will also apply the proper techniques of multimedia knowledge in developing a multimedia project. Students from other specialisations will also find this course attractive because multimedia technology has many applications in various fields such as, advertising, education, marketing, industrial and social.Course OutcomesAt the end of this course, students are able to:1. Define concepts and techniques of multimedia computing and its importance.2. Gain sufficient knowledge on the multimedia elements and its application.3. Apply the proper techniques of multimedia knowledge in developing a multimedia project. References1. Atul P. Godse, Dr. Deepali A. Godse. (2021). Computer Graphics and Multimedia. Technical Publications.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026156PENGATURCARAAN DEFENSIVE - TSS 3733DEFENSIVE PROGRAMMING - TSS 37333 Credit HoursPrerequisite: TSS 3353 Ethical HackerCourse SynopsisThis course introduces the principles and practices of defensive programming. Defensive programming means writing programs in a safe fashion to avoid vulnerabilities that attackers can exploit. It focuses on the secure software development process including designing secure applications, writing secure code, security application testing, common security vulnerabilities and security threats. Students will write and analyse code that demonstrates specific security development techniques.Course OutcomesAt the end of this course, students are able to:1. Determine the basic concepts of secure programming in PHP.2. Describe the most frequent programming errors leading to a software vulnerability.3. Design code that can protect against security threats and vulnerability.References1. Randy Connolly and Ricardo Hoar, Fundamentals of Web Development, Pearson Education Limited 20152. Website:http://www.w3schools.com/3. Paul Wellens, Practical Web Development, Pack Publishing, 20154. Larry Ullman, PHP and MySQL for Dynamic Websites, Fourth Edition, Visual QuickPro Guide.5. Don Gosselin, Diana Kokoska, Robert Easterbrooks, PHP Programming with MySQL, Second Edition, The Web Technology Series
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026157INTERNET BENDA – TSI 3363INTERNET OF THINGS – TSI 33633 Credit HoursPrerequisite: NoneCourse SynopsisThe objective of the course is to expose the students to the concept, task, technique and algorithm in of Internet of Things. Students will also be exposed to apply of Internet of Things techniques to a particular applications such as for smart and remote monitoring system. Internet of Things (IoT) is presently a hot technology worldwide. Government, academia, and industry are involved in different aspects of research, implementation, and business with IoT. IoT cuts across different application domain verticals ranging from civilian to defence sectors. These domains include agriculture, space, healthcare, manufacturing, construction, water, and mining, which are presently transitioning their legacy infrastructure to support IoT. IoT-based applications such as innovative shopping system, infrastructure management in both urban and rural areas, remote health monitoring and emergency notification systems, and transportation systems, are gradually relying on IoT based systems. Therefore, it is very important to learn the fundamentals of this emerging technology.Course OutcomesAt the end of this course, students are able to:1. Describe concepts and techniques of Smart Objects and IoT Architectures.2. Discover about various IOT-related programming and protocols.3. Develop simple IoT Systems using Arduino and Raspberry Pi.References1. Adeel Javed. (2016). Building Arduino Projects for the Internet of Things, Apress .2. Adrian McEwen, Hakim Cassimally. (2013). Designing the Internet of Things. Wiley3. Anuradha, J, Tripathy, B.K., I. (2018). Internet of things (IoT) : technologies, applications, challenges and solutions. CRC Presss: Taylor & Francis4. Dimitrios Serpanos, Marilyn Wolf, (2018). Internet-of-Things (IoT) Systems: Architectures, Algorithms, Methodologies.Springer International Publishing.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026158PERLOMBONGAN DATA - TSI 3323DATA MINING - TSI 33233 Credit HoursPrerequisite: NoneCourse SynopsisThe course introduces the concept and techniques of data mining. The main topics discussed include knowledge discovery process, data preprocessing and data mining techniques such as decision tree, regression, neural network, and clustering. The course will teach the students to find hidden pattern of information from large volume of data using selected data mining tools. Course OutcomesAt the end of this course, students are able to:1. Explain the fundamental concepts and models of data mining techniques.2. Analyse the given data set using appropriate data mining techniques.3. Describe the results of data mining techniques correctly and precisely.References1. Han, J. & Kamber, M. (2011). Data Mining: Concepts and Techniques. Third Edition.Massachusetts: Morgan Kaufmann Publisher.2. Georges, J. (2008). Applied Analytics using SAS Enterprise Miner Course Notes. Cary, NC: SAS Institute Inc.3. Tan, P.N., Steinbach, M. & Kumar, V. (2006). Introduction to Data Mining. Boston: Pearson Addison-Wesley.4. Cerrito, P.B. (2007). Introduction to Data Mining using SAS Enterprise Miner. Cary, NC: SAS Institute Inc.5. Roiger, R.J. & Geatz, M.W. (2003). Data Mining: A Tutorial-based Primer. Boston: Pearson Addison-Wesley.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026159E-DAGANG – TSI 3383E-COMMERCE – TSI 33833 Credit HoursPrerequisite: NoneCourse SynopsisThis course focuses on e-commerce principles from a business perspective, providing an overview of business and technology topics, business models, virtual chains, social innovation and marketing strategies. In addition, some major issues associated with e-commerce include security, privacy, intellectual property rights, authentication, encryption, acceptable use policies, and legal liabilities. Topics covered include E-business Models, E-business Infrastructure, Selling and Marketing on the Web, Web Server Hardware and Software, B2C and B2B strategies, Virtual Communities, Web Portals, E-commerce Software, Payment Systems, Social Media, Security and User Experience.Course OutcomesAt the end of this course, students are able to:1. Determining electronic commerce and the stakeholders and their capabilities and limitations in the strategic convergence of technology and business.2. Organise components, systems and/or processes to meet required specifications for a web presence.3. Develop awareness of ethical, social and legal aspects of e-commerce and propose features of existing e-commerce businesses, future directions or innovations for specific businesses.References1. Laudon, K.C,& Traver, C.G., (2020), E-Commerce 2020–2021: Business, Technology and Society, Global Edition, 16th edition, Pearson InternationalEC-Council. (2016).2. Hammersley, I. & Hammersley, M. (2018), Ultimate Guide To E-commerce Growth. Smartebusiness Ltd.3. Norfolk, M. & Holden, (2011) G. Starting an Online Business For Dummies, John Wiley & Sons Australia Ltd
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026160PENYIASATAN JENAYAH DIGITAL – TSF 3323DIGITAL CRIME INVESTIGATION – TSF 33233 Credit HoursPrerequisite: TSS 3323 Digital ForensicsCourse SynopsisIn this course, students gain knowledge on the field of computer crime. Basic criminal techniques, the relevant of laws, computer forensics will be introduced to the students. Students will also explore litigation such as depositions, expert reports and trials. This course is the students’ gateway into the world of investigating computer crimes.Course OutcomesAt the end of this course, students are able to:1. Evaluate various types of computer crimes based on the cyber laws of Malaysia.2. Utilise different computer forensic investigation techniques and tools to solve cyber crimes.3. Organise digital evidence from computer crime cases using computer forensic tools for the purpose of litigation.References1. Holt, T.J., Bossler, A.M. & Seigfried-Spellar, K.C. (2022). Cybercrime and Digital Forensics: An Introduction 3rd Edition. Routledge.2. Chung-Hao Chen, Wen-Chao Yang & Lijian Chen. (2021). Technologies to Advance Automation in Forensic Science and Criminal Investigation (Advances in Digital Crime, Forensics, and Cyber Terrorism). Information Science Reference.3. Easttom, C. & Taylor, J. (2011). Computer Crime, Investigation, and the Law. Boston: Course Technology.4. Malaysia & Board, I.L.B.S.L.R. (2001). Cyber Laws of Malaysia: Contains Digital Signature Act 1997 (Act 562), Computer Crimes Act 1997 (Act 563), Telemedicine Act 1997 (Act 564): as at 5 January 2001. Malaysia: International Law Book Services.5. Widup, S. (2014). Computer Forensics and Digital Investigation with EnCase Forensic. New York: McGraw-Hill Education.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026161FORENSIK RANGKAIAN – TSF 3723NETWORK FORENSICS – TSF 37233 Credit HoursPrerequisite: TSS 3323 Digital ForensicsTSS 3353 Ethical HackerTSS 3743 Network Intrusion Detection SystemCourse SynopsisThis course enables the understanding of how to recognise hackers' tracks and uncover the network-based evidence. It provides an explanation on how to uncover suspicious e-mail attachment from packet captures. The course also explores tracking intrusion via network and understanding of encryption-cracking attacks and other related tracking mechanism and techniques.Course Learning OutcomesAt the end of this course, students are able to:1. Review methodologies for managing any network forensics investigation.2. Manage forensic evidence from multiple communication devices i.e routers, firewalls and web proxies. 3. Construct a plan to manage network control in an organisation.References1. Jaswal, N. (2019). Hands-On Network Forensics: Investigate network attacks and find evidence using common network forensic tools. Packt Publishing Ltd. 2. Davidoff, S. & Ham, J. (2012). Network Forensics: Tracking Hackers Through Cyberspace.Westford: Pearson Education Inc. 3. Datt, S. (2016). Learning Network Forensics. Birmingham: Packt Publishing.4. Messier, R. (2017). Network Forensics. Indianapolis: Wiley.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026162PENGINTIPAN KETENTERAAN DAN INDUSTRI – TSF 3733MILITARY AND INDUSTRY ESPIONAGE – TSF 37333 Credit HoursPrerequisite: TSS 3323 Digital ForensicsCourse SynopsisIn this course, students will learn to define and describe the espionage. This module examines the motivations for military and industrial espionage and the various methods of attack on the physical security of an organisation, its electronic infrastructures and its staff and suppliers. Students will learn to analyse and mitigate potential attacks through military and industrial espionage, and will carry out risk management processes in military and industrial espionage.Course Learning OutcomesAt the end of this course, students are able to:1. Explain defense and countermeasures for potential attacks in the military and industrial espionage cases..2. Assess the concepts, types and characters of military and industrial espionage. 3. Build basic espionage behaviours and characteristics for analysing military and industrial espionage. References1. Pehlivan, O. K. (2018). Confronting Cyberespionage Under International Law. Routledge. 2. Clancy, T. & Greaney, M. (2012). Threat Vector. New York: Penguin Publishing Group.3. Stoll, C. (2012). Cuckoo’s Egg. New York: Knopf Doubleday Publishing Group.4. Brown, A. (2011). The Grey Line: Modern Corporate Espionage and Counter Intelligence. Columbus: Amur Strategic Research Group.5. Winkler, I. (2005). Spies Among Us: How to Stop the Spies, Terrorists, Hackers, and Criminals You Don’t Even Know You Encounter Every Day. Indianapolis: Wiley.6. Carr, J. (2011). Inside Cyber Warfare: Mapping the Cyber Underworld. Sebastopol: O’Reilly Media.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026163FORENSIK SISTEM PEMFAILAN – TSF 3313FILE SYSTEM FORENSICS – TSF 33133 Credit HoursPrerequisite: TSS 3323 Digital ForensicsCourse SynopsisIn this course, students gain knowledge on the basic concepts and theories of a volume and file system. Students will also learn how to implement it to an investigation. For each file system, students will learn different analysis techniques and special considerations that an investigator needs to decide. This course also teaches how the information could be used in an actual case scenario.Course OutcomesAt the end of this course, students are able to:1. Explain the components of volume and partition for various file systems analysis such as FAT, NTFS, Ext2 etc.2. Conduct digital investigation for file systems. 3. Assess file system forensics investigation by using file system forensics tools.References1. Carrier, B. (2005). File System Forensic Analysis. New Jersey: Pearson Education.2. Malin, C.H., Casey, E. & Aquilina, J.M. (2012). Malware Forensics Field Guide for Windows Systems: Digital Forensics Field Guides. Waltham: Elsevier Science.3. Daniel, L. (2011). Digital Forensics for Legal Professionals: Understanding Digital Evidence from the Warrant to the Courtroom. Waltham: Elsevier Science.4. Hoog, A. & Strzempka, K. (2011). iPhone and iOS Forensics: Investigation, Analysis and Mobile Security for Apple iPhone, iPad and iOS Devices. Waltham: Elsevier Science.5. Elrick, D., & Lockhart, K. (2014). Forensic Examination of Windows-Supported File Systems. California: CreateSpace Independent Publishing Platform.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026164FORENSIK DATA DAN MEDIA DIGITAL – TSF 3333DATA AND DIGITAL MEDIA FORENSICS – TSF 33333 Credit HoursPrerequisite: TSS 3323 Digital ForensicsCourse SynopsisThe area of digital media forensics is not just the art of finding deleted or hidden data but it is also the understanding of the underlying technologies behind the various tools used and the ability to present scientifically valid information. In this course, students will deal with the collection, preservation and analysis of digital media such that the evidence can be successfully presented in a court of law.Course OutcomesAt the end of this course, students are able to:1. Distinguish concept and methods to acquire digital evidence from various digital media.2. Practice the underlying technologies behind the various tools used in digital media analysis and forensics.3. Explain digital evidence using scientifically derived and proven methods that can be used to facilitate or further the reconstruction of events in an investigation.References1. Bommisetty, S., Tamma, R., Skulkin, O. & Mahalik, H. (2018). Practical Mobile Forensics. Third Edition. Birmingham: Packt Publishing.2. EC-Council. (2016). Computer Forensics: Investigating Data and Image Files (CHFI). Boston: Cengage Learning.3. EC-Council. (2016). Computer Forensics: Investigating File and Operating Systems, Wireless Networks, and Storage (CHFI). Boston: Cengage Learning.4. Schroader, A. & Cohen, T. (2011). Alternate Data Storage Forensics. Burlington: Elsevier Science.
JABATAN SAINS PERTAHANAN
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026138JABATAN SAINS PERTAHANANProfesor EmeritusProf. Emeritus Dato’ Dr. Wan Md Zin bin Wan YunusB.Sc. (Chemistry) (UKM), Pg. Dip. (Chemistry) (Kelsterton College, UK), M.Sc. (Analytical Chemistry) (Salford, UK), Ph.D. (Analytical Chemistry) (Salford, UK), D.P.S.K. ProfesorLt. Kol. Prof. Ts. Dr. Muhd Zuazhan bin YahyaB.Sc. (Hons.) (Physics with Education) (UM), M.Sc. (Solid State Ionics) (UM), Ph.D. (Advanced Materials) (UM)Pensyarah KananDr. Sharifah Aishah binti Syed AliBachelor of Mathematical Science (Hons.) (UIAM), M.Sc. (Mathematics) (UKM), Ph.D. (Management Science) (Strathclyde, UK)Ts. Dr. Fazilatulaili binti AliB.Sc. (Mathematics) (UKM), M.Sc. (Mathematics) (UKM), Ph.D. (Civil Eng. -Transport Operation Research) (Newcastle, UK)Ts. Dr. Nur Diyana binti KamarudinB.Eng. (Telecommunication Eng.) (UM), Pg. Dip. (Mobile and Satellite Comm.) (Surrey, UK), M.Sc. (Electrical and Electronic Eng.) (UPNM), Ph.D. (Image Processing and Computational Intelligence) (UTM)Ts. Dr. Syarifah Bahiyah Rahayu binti Syed MansoorBScBA in Computer Information System (NAU, US), Master of Information Tech. (QUT, AU), Ph.D. Information Science (UKM)Dr. Ruzanna binti Mat JusohB.Sc. (Mathematics) (UKM), M.Sc. (Mathematics) (UKM), Ph.D. (Civil Engineering) (Glasgow, UK)Ts. Dr. Mohd. Sidek Fadhil bin Mohd. YunusDip. of Computer Network and System (UniKL), B.IT. (Hons.) Computer System Security) (UniKL), M.Sc. Computer Science (UPNM), Ph.D. Computer Science (UPNM)Dr. Muhammad Naim bin AbdullahB.Comp.Sc. (Hons) (Computer System Security) (UPNM), M.Sc. (Computer Science) (UPNM), Ph.D. (Computer Science) (UPNM)Dr. Nurzulaikha binti Mahd Ab.lahB.Sc. (Mathematics) (USM), M.Sc. (Medical Statistics) (USM), Ph.D. (Biostatistics) (USM)Dr. Nurul Ain binti TohaB.Sc. (Hons) (Financial Mathematics) (Sheffield, UK), M.Sc. (Statistics and Computational Finance) (York, UK), Ph.D. (Statistical Science) (UCL, UK)PensyarahEncik Ahmad Shafiq bin Abdul RahmanBachelor of Mathematical Science (Hons.) (UIAM), M.Sc. (Teaching of Mathematics) (USM)
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026139OBJEKTIF DAN HASIL PEMBELAJARANPROGRAM SARJANA MUDA PENYELIDIKAN OPERASI DENGAN SAINS DATA (ZC33)Matlamat:Menghasilkan para graduan yang berdaya saing dari segi pengisian ilmu akademik dan berupaya mengintegrasikan sains asas dalam bidang pertahanan.Objektif Pembelajaran Program Programme Educational Objectives (PEO)PEO 1Menghasilkan individu yang mempunyai pengetahuan yang kukuh dan berkebolehan dalam menganalisis sesuatu perkara terutamanya dalam bidang Penyelidikan Operasi dengan Sains Data.Produce individuals with strong knowledge and ability to analyse a situation especially in the field of Operations Research with Data Science.PEO 2Melahirkan individu yang mempunyai kemahiran bekerja secara praktikal, mempunyai kemahiran komunikasi, kemahiran bekerja berkumpulan dan dapat mengaplikasikan kemahiran numerik dan digital untuk menyelesaikan masalah dalam bidang Penyelidikan Operasi dengan Sains Data.Produce individuals who have practical work skills, communication skills, group work skills and are able to apply numerical and digital skills to solve problems in the field of Operations Research with Data Science.PEO 3Melahirkan individu yang berkemahiran tinggi dari segi kepimpinan serta kemahiran pengurusan dan keusahawanan untuk membangunkan ilmu pengetahuan dan penyelidikan dalam bidang Penyelidikan Operasi dengan Sains Data.Produce highly skilled individuals in terms of leadership as well as management and entrepreneurial skills to develop knowledge and research in the field of Operations Research with Data Science.PEO 4Melahirkan individu yang bersikap positif, beretika dan profesional serta dapat meningkatkan atau memupuk jati diri dalam menghadapi persekitaran mencabar.Produce individuals with positive attitude, ethical and professional and able to improve or cultivate self-esteem in the face of challenging environments.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026140Hasil Pembelajaran Program Programme Learning Outcomes (PLO)PLO 1Graduan dapat mengaplikasikan pengetahuan dalam bidang Penyelidikan Operasi dengan Sains Data di pangkalan tentera, agensi dan industri lain.Graduates are able to apply knowledge in the field of Operations Research with Data Science at military bases, agencies and other industries.PLO 2Graduan mempunyai pemikiran kritikal untuk menyelesaikan masalah dalam bidang Penyelidikan Operasi dengan Sains Data.Graduates are able to think critically to solve Operations Research with Data Science problems.PLO 3Graduan dapat menggunakan pendekatan dan alat saintifik yang efisien dan berkesan dalam mengaplikasikan model dan algoritma Penyelidikan Operasi dengan Sains Data yang sesuai bagi sistem yang kompleks secara teknikal dan praktikal.Graduates are technically and practically competent in applying efficient and effective scientific approach and tools in appropriate Operations Research with Data Science models and algorithms of complex systems.PLO 4Graduan dapat mengaplikasikan kemahiran bekerja berkumpulan untuk mencapai matlamat yang sama.Graduates are able to apply working skills as an individual or as a team to achieve common goals.PLO 5Graduan dapat berkomunikasi secara efektif dalam bidang Penyelidikan Operasi dengan Sains Data di peringkat nasional dan antarabangsa.Graduates are able to communicate effectively in the field of Operations Research with Data Science at national and international levels.PLO 6Graduan dapat mengaplikasikan kemahiran numerik dalam bidang Penyelidikan Operasi dengan Sains Data menggunakan kemahiran digital.Graduates are able to apply numerical skills in the field of Operations Research with Data Science using digital skills.PLO 7 Graduan mempunyai ciri-ciri kepimpinan intelektual yang berkarakter.Graduates possess the attributes of intellectual leaders of characters. PLO 8Graduan dapat meningkatkan atau memupuk jati diri dalam menghadapi persekitaran mencabar. Graduates are able to enhance or cultivate self-esteem in the face of challenging environments.PLO 9Graduan dapat menggunakan kemahiran pengurusan dan keusahawanan.Graduates are able to apply management and entrepreneurial skills.PLO 10Graduan memiliki sikap positif, beretika, profesional dan peka terhadap alam sekitar.Graduates possess positive attitude, ethical, professional and sensitive to the environment.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026141STRUKTUR KURSUS DAN JUMLAH KREDIT KEPERLUAN PROGRAMSARJANA MUDA PENYELIDIKAN OPERASI DENGAN SAINS DATA (ZC33)JUMLAH KREDIT Jumlah keperluan kredit yang perlu dipenuhi untuk bergraduat adalah seperti mana jadual di bawah dan tempoh pengajian yang perlu diikuti adalah enam semester lazim dan dua semester pendek. Pecahan kursus yang perlu diambil adalah seperti berikut:KURSUS KREDITKursus Universiti:i. Kursus Teras Universitiii. Kursus Elektif Universiti206Kursus Teras Program:i. Teras Penyelidikan Operasiii. Teras Sains Data4818Kursus Elektif Program:i. Elektif Penyelidikan Operasiii. Elektif Sains Dataiii. Elektif Bersama Penyelidikan Operasi dengan Sains Data664Projek Tahun Akhir 6Latihan Industri 12JUMLAH KREDIT UNTUK BERGRADUAT 126KURSUS TERAS PROGRAMKURSUS TERAS PENYELIDIKAN OPERASIKursus-kursus Teras Penyelidikan Operasi adalah wajib diambil oleh semua pelajar Sarjana Muda Penyelidikan Operasi dengan Sains Data seperti berikut:KOD KURSUS NAMA KURSUS KREDITTPQ 3313 Calculus 3TPQ 3713 Decision Analysis 3TPQ 3723 Defence Logistics and Transportation Management 3TPQ 3743 Efficiency and Productivity Analysis 3TPQ 3353 Introduction to Programming 3
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026142KURSUS TERAS SAINS DATAKursus-kursus Teras Sains Data adalah wajib diambil oleh semua pelajar Sarjana Muda Penyelidikan Operasi dengan Sains Data seperti berikut:TPQ 3753 Game Theory 3TPQ 3363 Linear Algebra 3TPQ 3373 Linear and Integer Programming 3TPQ 3383 Network Flow 3TPQ 3393 Numerical Analysis 3TPQ 3403 Regression Analysis 3TPQ 3413 Simulation and Queuing Theory 3TPQ 3423 Statistical Computing with R 3TPQ 3783 Statistical Inference 3TPQ 3433 Statistics for Operations Research 3TPQ 3793 Time Series and Forecasting 3JUMLAH KREDIT 48KOD KURSUS NAMA KURSUS KREDITTPD 3833 Big Data Analytics 3TPD 3853 Cloud and Data Centre Administration 3TPD 3813 Database Concept 3TPD 3843 Data Quality Management 3TPD 3823 Fundamentals of Artificial Intelligence 3TPD 3713 Machine Learning for Data Science 3JUMLAH KREDIT 18
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026143KURSUS ELEKTIF PENYELIDIKAN OPERASIBagi kursus Elektif Program Penyelidikan Operasi, pelajar perlu memilih sebanyak 6 kredit sahaja. Kursus-kursus Elektif Program Penyelidikan Operasi adalah seperti berikut:KURSUS ELEKTIF SAINS DATABagi kursus Elektif Program Sains Data, pelajar perlu memilih sebanyak 6 kredit sahaja. Kursuskursus Elektif Program Sains Data adalah seperti berikut:KURSUS ELEKTIF BERSAMA PENYELIDIKAN OPERASI DENGAN SAINS DATABagi kursus Elektif Program Bersama, pelajar perlu memilih sebanyak 4 kredit sahaja. Kursuskursus Elektif Program Bersama adalah seperti berikut:KURSUS ELEKTIF PROGRAMKOD KURSUS NAMA KURSUS KREDITTPQ 3713 Econometrics 3TPQ 3723 Introduction to Materials Management 3TPQ 3733 Project Management 3KOD KURSUS NAMA KURSUS KREDITTPD 3713 Business Intelligence with Data Visualisation 3TPD 3723 Data Transmission Assurance and Security 3TPD 3733 Image Processing and Analytics 3TPD 3743 Object-oriented Application 3KOD KURSUS NAMA KURSUS KREDITTPB 3712 Data Visualisation with Canva 2TPB 3722 Data Analytics in Finance 2TPB 3732 Data Exploration with Excel 2TPB 3742 Entrepreneurial Analytics 2TPB 3752 IoT Data Analytics 2
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026144Projek tahun akhir adalah wajib diambil oleh semua pelajar Sarjana Muda Penyelidikan Operasi dengan Sains Data seperti berikut:Latihan industri adalah wajib diambil oleh semua pelajar Sarjana Muda Penyelidikan Operasi dengan Sains Data seperti berikut:TPB 3762 Research Methodology 2TPB 3772 History of Mathematics 2PROJEK TAHUN AKHIRSARJANA MUDA PENYELIDIKAN OPERASI DENGAN SAINS DATA (ZC33)KOD KURSUS NAMA KURSUS KREDITTPQ 3332 Final Year Project I 2TPQ 3344 Final Year Project II 4JUMLAH KREDIT 6LATIHAN INDUSTRISARJANA MUDA PENYELIDIKAN OPERASI DENGAN SAINS DATA (ZC33)KOD KURSUS NAMA KURSUS KREDITTPQ 331C Industrial Training 12JUMLAH KREDIT 12
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026145TAHUN PERTAMASEMESTER 1 SEMESTER 2KOD KURSUS NAMA KURSUS KREDIT KOD KURSUS NAMA KURSUS KREDITDUS3042 Military History and Leadership 2 MPU3132 Appreciation of Ethics and Civilizations 2MPU3142 Philosophy and Currents Issues 2 LLF3XX1 Foreign Language II 1MPU3322/MPU3332/ MPU3342Blue Ocean Strategy and Total Defence/ Fiqh Keutamaan / Integrity and Anti – Corruption 2 TPQ3373 Linear and Integer Programming 3LLF3XX1 Foreign Language I 1 TPQ3413 Simulation and Queuing Theory 3TPQ3313 Calculus 3 TPD3813 Database Concept 3TPQ3353 Introduction toProgramming 3 TPD3843 Data Quality Management 3TPQ3363 Linear Algebra 3 TPB3XX2 Co-Elective I 2TPQ3433 Statistics for Operations Research 3ALK3112/ PLS3121/ QKA3121Latihan Ketenteraan Umum/ PALAPES/ Kesatria Al-Fateh 22/1 PLS3111/ QKA3111PALAPES/Kesatria Al-Fateh 1 1JUMLAH KREDIT 20 JUMLAH KREDIT 19/18SEMESTER PENDEKKOD KURSUS NAMA KURSUS KREDITTPX 37X3 Elective I 3TPQ 3393 Numerical Analysis 3TPQ 3423 Statistical Computing with R 3JUMLAH KREDIT 9STRUKTUR KURIKULUM PROGRAM SARJANA MUDA PENYELIDIKAN OPERASI DENGAN SAINS DATA (ZC33)
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026146TAHUN KEDUASEMESTER 3 SEMESTER 4KOD KURSUS NAMA KURSUS KREDIT KOD KURSUS NAMA KURSUS KREDITDUS3022 Introduction to Strategic Studies 2 LLE3022 Al-Ghazali’s Dialogue: English Communication 2LLE3012 English for Academic Writing 2 MPU3212 Basic Entrepreneurship 2MPU3412/ MPU3422/ MPU3432Human Movement Science / Community Service / Nationhood in World Politics2 TPQ3723 Defence Logistics and Transportation Management3TPQ3713 Decision Analysis 3 TPQ3743 Efficiency and Productivity Analysis 3TPD3713 Machine Learning for Data Science 3 TPQ3403 Regression Analysis 3TPQ3783 Statistical Inference 3 TPD3823 Fundamentals of Artificial Intelligence 3TPX37X3 Elective II 3 TPX37X3 Elective III 3ALK3122/ PLS3131/ QKA3132Latihan Ketenteraan Umum/ PALAPES/ Kesatria Al-Fateh 32/1 TPB3XX2 Co- Elective II 2QKS3172/ PLS3141/ QXXYYY2Tempur Tanpa Senjata / PALAPES/ Ko-Kurikulum2 / 1 / 2JUMLAH KREDIT 20/19 JUMLAH KREDIT 23/22SEMESTER PENDEKKOD KURSUS NAMA KURSUS KREDITTP3 3332 Final Year Project I 2TPD 3853 Cloud and Data Centre Administration 3TPX 37X3 Elective IV 3JUMLAH KREDIT 8
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026147TAHUN KETIGASEMESTER 5 SEMESTER 6KOD KURSUS NAMA KURSUS KREDIT KOD KURSUS NAMA KURSUS KREDITTPQ 3344 Final Year Project II 4 TPQ 331C Industrial Training 12TPQ 3753 Game Theory 3TPQ 3383 Network Flow 3TPQ 3793 Time Series and Forecasting 3TPD 3833 Big Data Analytics 3PLS 3151 PALAPES 1PLS 3161 PALAPES 1JUMLAH KREDIT 18 JUMLAH KREDIT 12
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026148SINOPSIS KURSUS TERAS PENYELIDIKAN OPERASICourse Code: TPQ 3313Course Name: KALKULUSCALCULUS Credit Hour: 3Pre-requisite: NoneCourse SynopsisThis is the standard first-semester mathematics course. The emphasis in this course is on problem solving, not the theory of analysis. There should be some understanding of analysis, but the majority of the proofs in the text should not be covered in class. The syllabus for this course includes most of the basic topics on functions and graphs, limits and continuity, techniques of differentiation and integration and its applications.Course Learning OutcomesAt the end of this course, students are able to:1. USE the properties of functions, limits and continuity, techniques of differentiation and integration. (C3)2. SOLVE problems limits, continuity, derivatives, and integrals. (C3)3. SKETCH the graph of a polynomial or rational function using the technique learned in calculus. (P4)References1. Stewart, J. (2021). Calculus. 9th edition. Boston. Cengage Learning.2. Thomas, G., Weir, M., Hass, J. & Heil, C. (2014). Calculus Early Transcendental Single Variable. Thirteenth Edition. New York: Pearson.3. Anton, H., Bivens, I. & Davis, S. (2012). Calculus Early Transcendental Single Variable. Tenth Edition. New York: John Wiley & Sons.4. Larson, R. & Edwards, B.H. (2010). Calculus. Ninth Edition. Belmont: Brooks/Cole.5. Strauss, M., Bradley, G. & Smith, K. (2002). Calculus. Third Edition. Upper Saddle River, New Jersey: Prentice Hall.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026149Course Code: TPQ 3713Course Name: ANALISIS KEPUTUSAN DECISION ANALYSISCredit Hour: 3Pre-requisite: NoneCourse SynopsisThis course introduces logical frameworks for designing and assessing decision situations to achieve action clarity. It covers formulating creative alternatives, describing unpredictable events, and incorporating decision-makers' values and preferences. Students will learn a collection of logical tools for framing problems, conducting analyses, and laying the groundwork for decision analytic modeling. Topics include decision making under certainty, decision making under uncertainty, decision making under risk, decision trees, multi criteria decision making, the value of information and sensitivity analysis. The course is designed to equip students with the knowledge and skills necessary to make informed and structured decisions in both personal and professional contexts.Course Learning OutcomesAt the end of this course, students are able to:1. DESCRIBE the basic elements of a decision problem. (C2, P1).2. CREATE graphical structure (decision tree and influence diagram) for a decision model. (C5, P7)3. APPLY decision analysis tools and techniques for analysing a decision model. (C3).References1. Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2019). An introduction to management science: quantitative approaches to decision making. United States: Cengage learning.2. R. A.Howard, R. A., and Abbas, A. E. (2016). Foundations of Decision Analysis. England: Pearson. 3. W. Taylor III, B. (2019). Introduction to Management Science. Thirteenth Edition. United Kingdom: Pearson. 4. Taha, H. A. (2023). Operations Research: An Introduction. Eleventh Edition. United States: Pearson. 5. Render, B., Jr, R.M.S., Hanna, M.E. & Hale, T.S. (2018). Quantitative Analysis for Management. Thirteenth Edition. Upper Saddle River, New Jersey: Pearson.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026150Course Code: TPQ 3723Course Name: LOGISTIK PERTAHANAN DAN PENGURUSAN PENGANGKUTAN DEFENCE LOGISTICS AND TRANSPORTATION MANAGEMENTCredit Hour: 3Pre-requisite: NoneCourse SynopsisThis course introduces quantitative techniques and practices of Operations Research for strategic and tactical design, management of logistical, and transportation system. A variety of passenger and flight systems related to air, motor and rail systems will be discussed. The practice of revenue management, fleet assignment and crew scheduling in airlines industries are included. Topics such as transportation, transshipment, and stocking and supply chain design will be explored. Course Learning OutcomesAt the end of this course, students are able to:1. IDENTIFY the importance of logistics and transportation management. (C1)2. APPLY the theoretical knowledge to practical issues in logistics and transportation management. (C3)3. EVALUATE knowledge/ science within the area of logistics and transportation management. (C4)References1. Coyle, J. J. (2018). Transportation: A Global Supply Chain Perspective. 9th Ed. Cengage Learning.2. Novack, R.A., Gibson, B., Suzuki, Y. & Coyle, J.J. (2018). Transportation: A Global Supply Chain Perspective. Ninth Edition. Boston: Cengage Learning.3. Hess, E.J. (2017). Civil War Logistics: A Study of Military Transportation. Baton Rouge: LSU Press.4. Mannering, F.L. & Washburn, S.S. (2012). Principles of Highway Engineering and Traffic Analysis. Fifth Edition. Hoboken, New Jersey: John Wiley & Sons, Inc.5. Hazen, J.K. & Lynch, C.F. (2008). Role of Transportation in Supply Chain. Memphis, Tennessee: CFL Publishing.6. Kasilingam, R.G. (1998). Logistics and Transportation: Design and Planning. Dordrecht, Netherlands: Springer Science+Business Media.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026151Course Code: TPQ 3743Course Name: ANALISIS KECEKAPAN DAN PRODUKTIVITI EFFICIENCY AND PRODUCTIVITY ANALYSIS Credit Hour: 3Pre-requisite: NoneCourse SynopsisThis course introduces Data Envelopment Analysis (DEA), a frontier-based, non-parametric methodology used for performance evaluation and benchmarking that compares the efficiency of various entities against best and worst practice frontiers. Throughout the course, the students will learn performance management concepts, evaluation methodologies, basic DEA models and applications, and their practical applications in various business contexts.Course Learning OutcomesAt the end of this course, students are able to:1. DESCRIBE the fundamental concepts of Data Envelopment Analysis (DEA). (C2)2. APPLY appropriate DEA models to assess the performance of various entities or organisations in different contexts. (C3)3. ANALYSE data and DEA results to make informed decisions regarding the performance of organisations or processes. (C4) References1. Kao, C. (2023). Network Data Envelopment Analysis: Foundations and Extensions (2ndEdition). International Series in Operations Research and Management Science. 2. Kao, C. (2017). Network Data Envelopment Analysis: Foundations and Extensions (1stEdition). International Series in Operations Research and Management Science. 3. Zhu, J. (2009). Quantitative Models for Performance Evaluation and Benchmarking: DataEnvelopment Analysis with Spreadsheets (Vol. 2). New York: Springer.4. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-solver software (Vol. 2, p. 489). New York: Springer.5. Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to data envelopment analysis and its uses: with DEA-solver software and references. Springer Science & Business Media.6. Timothy, J. C., Prasada Rao, D. S., Christopher, J. O. and George E. B. (2005). An Introduction to Efficiency and Productivity Analysis 2nd Edition. Springer7. Coelli, T. J., Rao, D. S. P., O'Donnell, C. J., & Battese, G. E. (2005). An Introduction to Efficiency and Productivity Analysis. Springer Science & Business Media.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026152Course Code: TPQ 3353Course Name: PENGENALAN KEPADA PENGATURCARAANINTRODUCTION TO PROGRAMMINGCredit Hour: 3Pre-requisite: NoneCourse SynopsisThis course aims to introduce the basic principles of programming concepts and programming structure. It covers the following topics: the introduction of computer programming and language programming, algorithms, primitive data types and operations, selection statements, loops, functions and arrays. The practical part of this course is covered in the lab through exercises and practical assignments. Course Learning OutcomesAt the end of this course, students are able to:1. IDENTIFY basic concepts in Java programming language. (C1)2. WRITE Java programming algorithm and code to solve an identified problem. (A2)3. PRACTICE coding a program correctly and effectively through Java programming language. (P3)References1. Charatan, Q. and Kans, A. (2019). Java in Two Semesters: Featuring Java FX. 4th edition. Springer Nature Switzerland AG.2. Farrell, J. (2016). JAVA Programming. 8th edition. Course Technology: Cengage Learning.3. Deitel, P. and Deitel, H. (2013). Java How to Program. 10th edition. Pearson.4. Hortsmann, C. (2016). Big Java. 6th edition. John Wiley & Sons.5. Liang, Y. D. (2015). Introduction to Java Programming. 10th edition. Pearson Education Limited.6. Malik, D.S. (2012). Java: Programming: From Problem Analysis to Programming Design. 5th edition. Course Technology: Cengage Learning.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026153Course Code: TPQ 3753Course Name: TEORI PERMAINAN GAME THEORY Credit Hour: 3Pre-requisite: NoneCourse SynopsisThis course aims to show students that decision problems with a limited number of alternatives can be solved by using decision analysis techniques. A study of problem-solving processes using principles and analytical methods of measurement, calculation, control and assessment systems enable students to increase their potential and abilities in managerial decision-making. Mastering data analysis, modelling, and spreadsheet use with data analysis and decision making with Microsoft Excel. For decision problems with uncertainty, criteria that reflect decision maker's attitude towards risks are used. Game theory is used to obtain the best decision for two competitors with contradicting goals, under each competitor's worst condition. Course Learning OutcomesAt the end of this course, students are able to:1. APPLY decision making technique under certainties and uncertainties. (C3)2. USE the tools of decision analysis for a strategic decision by combining mathematical strategies with intuitive decision making. (C3, A1)3. DEMONSTRATE the solutions for decision and game theory problem. (C3, A3)References1. Jeffrey Carpenter and Andrea Robbett. (2022). Game Theory and Behavior. The MIT Press.2. Howard, R.A. & Abbas, A.E. (2016). Foundations of Decision Analysis. Essex: Pearson. 3. Watson, J. (2013). An Introduction to Game Theory. Third Edition. New York: W.W. Norton & Company, Inc. 4. Render, B., Jr, R.M.S., Hanna, M.E. & Hale, T.S. (2015). Quantitative Analysis for Management. Thirteenth Edition. Upper Saddle River, New Jersey: Pearson. 5. Winston, W.L. (2004). Operations Research: Applications and Algorithms. Fourth Edition. New York: Cengage Learning, Inc.6. Binmore, K. (2007). Playing for Real: A Text on Game Theory. New York: Oxford University Press.7. Clement, R.T. (1997). Making Hard Decisions: An Introduction to Decision Analysis. Second Edition. Belmont, California: Duxbury Press.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026154Course Code: TPQ 3363Course Name: ALJABAR LINEARLINEAR ALGEBRA Credit Hour: 3Pre-requisite: NoneCourse SynopsisLinear algebra is a branch of mathematics concerned with the study of systems of linear equations and the properties of matrices. The concepts of linear algebra are extremely useful in physics, economics, social sciences, natural sciences, and engineering. Topics for this course include systems of linear equations and matrices, determinants, vector spaces, eigenvalues and eigenvectors, product spaces and linear transformations.Course Learning OutcomesAt the end of this course, students are able to:1. IDENTIFY the basic ideas of linear algebra including concepts of linear systems, determinants, Euclidean and general vector spaces, eigenvalues, eigenvectors, inner product spaces and linear transformations. (C1)2. PERFORM system of linear equations. (C2)3. EXAMINE basic linear algebra techniques learned from the chosen topics to solve a variety of practical problems. (C4)References1. Strang, G. (2021). Introduction to Linear Algebra. Fifth Edition (revised). Wellesley, United States: Wellesley-Cambridge Press.2. Anton, H. (2014). Elementary Linear Algebra: with Supplemental Applications. Eleventh Edition. New York: John Wiley & Sons.3. Anthony, M. & Harvey, M. (2012). Linear Algebra: Concepts and Methods. New York: Cambridge University Press.4. Anton, H. & Rorres, C. (2010). Elementary Linear Algebra with Supplemental Applications. Tenth Edition. New York: John Wiley & Sons.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026155Course Code: TPQ 3373Course Name: PENGATURCARAAN LINEAR DAN INTEGER LINEAR AND INTEGER PROGRAMMING Credit Hour: 3Pre-requisite: Linear Algebra - TPQ 3363Course SynopsisLinear and integer programming involves the formulation and solution of fundamental optimization models that are widely used in practice. This course covers the fundamentals of linear and integer programming theory, algorithms and software. Topics studied in the linear programming part include graphical solution methods, simplex methods, duality, sensitivity analysis, and several special types of linear programming problems such as transportation and assignment models. Building on linear programming, the second part of this course introduces solution strategies in integer programming, such as branch-and-bound and cutting-plane procedures.Course Learning OutcomesAt the end of this course, students are able to:1. DESCRIBE the basic concepts and methods of linear and integer programming. (C2)2. APPLY linear and integer programming techniques to solve and model some basic decisionmaking problems arising in daily business life. (C3)3. ANALYSE the linear and integer programming model's duality and sensitivity. (C4)References1. Taha, H. A. (2023). Operations Research: An Introduction. Eleventh Edition. United States: Pearson. 2. W. Taylor III, B. (2019). Introduction to Management Science. Thirteenth Edition. United Kingdom: Pearson. 3. Render, B., Jr, R.M.S., Hanna, M.E. & Hale, T.S. (2018). Quantitative Analysis for Management. Thirteenth Edition. Upper Saddle River, New Jersey: Pearson. Gupta, P.K. & Hira, D.S. (2007). Operations Research. Twenty-Second Edition. New Delhi: S. Chand & Company Ltd. 4. Winston, W.L. (2004). Operations Research: Applications and Algorithms. Fourth Edition. Boston: Cengage Learning.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026156Course Code: TPQ 3383Course Name: ALIRAN RANGKAIAN NETWORK FLOW Credit Hour: 3Pre-requisite: NoneCourse SynopsisThis course is an undergraduate-level subject in the theory and practice of network flows. Network flow problems form a substance of linear programming problems that are used to represent a broad variety of application areas, which include manufacturing, transportation, project activities, and finance etc. Students will learn the fundamental concepts of graph and network problems, theories, solution methods, algorithm complexity, and various applications of network flow problems.Course Learning OutcomesAt the end of this course, students are able to:1. DESCRIBE the fundamental theories and principles of network flow problems, including theconcepts of graphs and networks. (C2)2. DEVELOP a graphical representation of network flow, including nodes, edges, capacities and flow values. (C5)3. APPLY different solution methods and algorithms to solve network flow problems across arange of application domains. (C4)References1. Taha, H.A. (2017). Operations Research: An Introduction. Tenth Edition. Boston, United States: Pearson.2. Bazaraa, M.S., Jarvis, J. J. & Sherali, H. D. (2010). Linear Programming and Network Flow, 4th Edition, John Wiley & Sons. 3. Ahuja, R.K., Magnanti, T.L. & Orlin, J.B. (1993). Network Flows: Theory, Algorithms, and Applications. New Jersey: Pearson.4. Murty, K.G. (1992). Network Programming. Upper Saddle, New Jersey: Prentice-Hall, Inc.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026157Course Code: TPQ 3393Course Name: ANALISIS NUMERIK NUMERICAL ANALYSISCredit Hour: 3Pre-requisite: NoneCourse SynopsisThis course introduces general-purpose numerical methods concepts for solving problems in Operations Research and Engineering. Students should develop an understanding of the strengths and limitations of standard numerical techniques applied to problems in Operations Research and Engineering. Topics discussed will be roots of nonlinear equations, systems of linear equations, regression and interpolation, numerical differentiation and integration, ordinary differential equation, and optimisation. MATLAB commands will be introduced to solve numerical problems.Course Learning OutcomesAt the end of this course, students are able to:1. IDENTIFY the most common numerical methods used in operations research and engineering. (C1)2. ASSESS the efficiency of a selected numerical method when more than one option is available to solve a certain class of problem. (C3)3. DEMONSTRATE the convergence properties, limitation of different numerical methods and implement using MATLAB’s programming language. (C3, P5)References1. Chapra, S.C. & Canale, R.P. (2021). Numerical Methods for Engineers. Eight Edition. New York, United States: McGraw-Hill Education.2. Butenko, S. & Pardalos, P.M. (2014). Numerical Methods and Optimisation: An Introduction. Boca Raton, Florida: Chapman and Hall/CRC.3. Chapra, S.C. (2012). Applied Numerical Methods with MATLAB for Engineers and Scientists. Third Edition. New York: McGraw-Hill Education.4. Burden, R.L. & Faires, J.D. (2005). Numerical Analysis. Eighth Edition. New York: Thomson Brooks/Cole.5. Rao, S.S. (2002). Applied Numerical Methods for Engineers and Scientists. Upper Saddle River, New Jersey: Prentice-Hall.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026158Course Code: TPQ 3403Course Name: ANALISIS REGRESI REGRESSION ANALYSIS Credit Hour: 3Pre-requisite: Statistics For Operations Research - TPQ 3433Course SynopsisThe prime objective of this course is to provide the basic regression analysis such as linear regression, model selection, and logistic regression. More advanced topics including generalised linear regression and nonparametric regression will be covered. Students will be presented fundamental exposure in the practical use of some computer software to correctly analyse problems. This course will focus on developing mathematical modelling from the given data. Students are supervised on developing solutions to assist in communicating the desired results to the decision makers.Course Learning OutcomesAt the end of this course, students are able to:1. IDENTIFY the theoretical foundations of basic concepts in probability and statistics. (A4)2. APPLY the theoretical foundations of the knowledge to model pertaining to related problems. (C3)3. SOLVE the mathematical models using various tools and methods from the theoretical foundations on basic concepts in probability and statistics together with statistical software, in order to interpret the results from the analysis. (C3)References1. Montgomery, D. C., Peck, E. A. & Vining, G. (2021). Introduction to Linear Regression Analysis. 6th Edition. Wiley Series in Probability and Statistics.2. Navidi, W. (2015). Statistics for Engineers & Scientists. Fourth Edition. New York: McGrawHill Education.3. Mendenhall, W. & Sincich, T. (2011). A Second Course in Statistics: Regression Analysis. Seventh Edition. London: Pearson Education Limited.4. Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models. McGraw-hill.5. Seber, G.A.F. & Lee, A.J. (2003). Linear Regression Analysis. Second Edition. Hoboken, New Jersey: John Wiley & Sons, Inc.6. Draper, N. R., & Smith, H. (1998). Applied regression analysis (Vol. 326). John Wiley & Sons.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026159Course Code: TPQ 3413Course Name: SIMULASI DAN TEORI GILIRAN SIMULATION AND QUEUING THEORY Credit Hour: 3Pre-requisite: NoneCourse SynopsisThis course exposes students to analyse problem, define data input, and develop a discrete simulation model to make a decision. Students will be able to distinguish between discrete and continuous system, stochastic and probability, and dynamic and static. Students will be broadly taught to use statistical technique to analyse the simulation output, support the simulation model, and compare the simulation model with the real system.Course Learning OutcomesAt the end of this course, students are able to:1. RECOGNISE the main analytical techniques used in queuing systems. (C2)2. DIFFERENTIATE the basic issues and methodologies used in modelling real problems. (C4, P1)3. APPLY statistical aspects of simulation and queuing systems and use simulation software for the solution of queuing models. (C3)References1. Taha, H. A. (2023). Operations Research: An Introduction. Eleventh Edition. United States: Pearson.2. John F. Shortle, James M. Thompson, Donald Gross, Carl M. Harris. Fundamentals of Queueing Theory (2018). 5th Edition. Wiley Series in Probability and Statistics.3. Bhat, U. Narayan, (2015). An Introductory to Queueing Theory. Birkhauser. 4. Law, A.M. (2014). Simulation Modeling and Analysis. Fifth Edition. New York: McGraw-Hill Education.5. Rossetti, M.D. (2016). Simulation Modeling and Arena. Second Edition. Hoboken, New Jersey: John Wiley and Sons, Inc.6. Gross, D., Shortle, J.F., Thompson, J.M. & Harris, C.M. (2008). Fundamentals of Queuing Theory. Fourth Edition. Hoboken, New Jersey: John Wiley and Sons, Inc.
Buku Panduan Akademik, FSTP Sesi Akademik 2025/2026160Course Code: TPQ 3423Course Name: PENGKOMPUTERAN STATISTIK DENGAN RSTATISTICAL COMPUTING WITH RCredit Hour: 3Pre-requisite: NoneCourse SynopsisStatistical Computing with R is an introductory course that aims to equip undergraduate students with the fundamental knowledge and skills required to perform data analysis, statistical modeling, and data visualization using the R programming language. The course will cover the basics of R programming, data manipulation, exploratory data analysis, statistical inference, and visualization techniques. Students will gain hands-on experience through practical exercises and projects, enabling them to apply statistical techniques and programming skills to real-world data sets.Course Learning OutcomesAt the end of this course, students are able to:1. COMPREHEND the basic of the R programming language and its applications in statistical computing, enabling students to manipulate data, perform statistical analyses, and create visualizations using R. (C2)2. EMPLOY the ggplot2 or other libraries in R to analyze and interpret data using statistical techniques,and effectively communicate insights through data visualization libraries in R. (C3)3. DEVELOP a foundational understanding of statistical concepts, including hypothesis testing, confidence intervals, and regression analysis, and apply these techniques to draw meaningful conclusions from data and make data-driven decisions. (C5)References1. Rizzo, M. L. (2019). Statistical Computing with R. Second Edition. Boca Raton, FL: CRC Press2. Braun, W. J., & Murdoch, D. J. (2021). A first course in statistical programming with R. Cambridge University.3. Loftus, S. C. (2021). Basic statistics with R: reaching decisions with data. Academic Press.