BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PENGURUSAN KESELAMATAN MAKLUMAT - TSS 3333
INFORMATION SECURITY MANAGEMENT - TSS 3333
3 Credit Hours
Prerequisite: None
Course Synopsis
The course introduces an understanding of information security management concepts,
including planning for security, security policy, risk management, law and ethics and advanced
cryptography. It will also provide the knowledge and skills needed to plan the implementation of
an information security management system that provides efficient, effective safeguards and
responds to the organisation’s needs.
Course Learning Outcomes
At the end of this course, students are able to:
1. Understand the concepts of information security management.
2. Identify the issues and risks related to information security.
3. Provide understanding on security policy and resources involved in identifying secure
networks.
4. Understand standard methods and metrics in managing information security.
References
1. Whitman, M. & Mattord, H. (2010). Management of Information Security. Third Edition.
Boston: Thomson Course Technology.
2. Purser, S. (2004). A Practical Guide to Managing Information Security. London: Artech
House.
3. Krause, M. & Tipton, H.F. (2012). Security Handbook of Information Management. Sixth
Edition. Boca Raton: Auerbach Publications.
4. Dhillon, G. (2007). Principles of Information System Security. New York: Wiley.
5. Andress, J. (2011). The Basics of Information Security: Understanding the
Fundamentals of Information Security in Theory and Practice. Maryland Heights:
Syngress.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
KESELAMATAN RANGKAIAN WAYARLES - TSS 3343
WIRELESS NETWORK SECURITY - TSS 3343
3 Credit Hours
Prerequisite: None
Course Synopsis
Securing 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 Outcomes
At the end of this course, students are able to:
1. Understand the concepts in wireless networking, protocols and standards.
2. Describe about threats faced by wireless networks.
3. Apply the concepts in planning, designing and implementing of a secure network.
4. Analyse and audit wireless network security using wireless network analysis tools.
References
1. 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: Sybex.
4. Vacca, J.R. (2006). Guide to Wireless Network Security. Berlin: Springer.
5. Wrightson, T. (2012). Wireless Network Security A Beginner's Guide. New York:
McGraw-Hill Osborne Media.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PENGGODAM BERETIKA - TSS 3353
ETHICAL HACKER - TSS 3353
3 Credit Hours
Prerequisite: None
Course Synopsis
The 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 Outcomes
At the end of this course, students are able to:
1. Understand what an ethical hacker can and cannot do legally.
2. Learn more on hacking techniques and to protect the computer and network.
3. Understand vulnerabilities in network and system.
4. Provide the best solution for their computer and network.
References
1. EC-Council. (2010). Network Defense: Security and Vulnerability Assessment. Boston:
Course Technology Press.
2. Harris, S., Harper, A., Eagle, C. & Ness, J. (2011). Gray Hat Hacking: The Ethical
Hacker’s Handbook. Third Edition. New York: McGraw-Hill/Osborne.
3. Krutz, R.L. & Vines, R.D. (2008). The CEH Prep Guide: The Comprehensive Guide to
Certified Ethical Hacking. New York: Wiley Publishing.
4. McClure, S., Scambray, J. & Kurtz, G. (2009). Hacking Exposed Network Security
Secrets and Solutions. Sixth Edition. New York: McGraw-Hill/Osborne.
5. Simpson, M.T. (2011). Hands-on Ethical Hacking and Network Defense. Second Edition.
Boston: Course Technology Press.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SINOPSIS KURSUS ELEKTIF
PROGRAM IJAZAH SARJANA MUDA SAINS KOMPUTER
(KESELAMATAN SISTEM KOMPUTER) (ZC27)
MULTIMEDIA - TSI 3713
MULTIMEDIA - TSI 3713
3 Credit Hours
Prerequisite: None
Course Synopsis
Students 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 Outcomes
At 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. Distinguish the appropriate multimedia techniques in a multimedia project development.
4. Apply the proper techniques of multimedia knowledge in developing a multimedia
project.
References
1. Gerald, F. & Ramesh, J. (2014). Multimedia Computing. Boston: Cambridge University
Press.
2. Cancellero, J. (2006). Exploring Sound Design for Interactive Media. Canada: Thomson
Delmar Learning.
3. Shaner, P. & Jone, G.E. (2004). Real World Video. Second Edition. San Francisco:
Peachpit Press.
4. Ozer, J. (2004). Guide to Digital Video. New York: Wiley Publishing.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
INTERAKSI MANUSIA-KOMPUTER - TSI 3723
HUMAN-COMPUTER INTERACTION - TSI 3723
3 Credit Hours
Prerequisite: None
Course Synopsis
This course is an introduction to the fundamentals of human-computer interaction, user interface
design and usability analysis. Students will learn principles and guidelines for usability,
quantitative and qualitative analysis methods besides apply them through critiques of existing
interfaces and development of new ones. Topics covered will also include cognitive models and
visual models. Students will learn the principles of visual design and prototyping methods that
inform effective interaction design.
Course Outcomes
At the end of this course, students are able to:
1. Distinguish between the types of knowledge produced in science, engineering and
design.
2. Explain the theoretical perspectives on cognition and human behaviour that are relevant
to the design of interactive systems.
3. Design an experimental study.
4. Develop one or more prototypes of a design by using prototypes in the design process.
References
1. Sharp, H., Rogers, Y. & Preece, J. (2015). Interaction Design: Beyond Human-Computer
Interaction. Fourth Edition. West Sussex: John Wiley & Sons.
2. Shneiderman, B., Plaisant, C., Cohen, M. & Jacobs, S. (2016). Designing the User
Interface: Strategies for Effective Human Computer Interaction. Six Edition. Boston:
Addison-Wesley.
3. Don, N. (2013). The Design of Everyday Things. Revised and Expanded Edition. New
York: Basic Books.
4. Solis, T. (2016). Human-Computer Interaction: The Fundamentals Made Easy!
Charleston: CreateSpace Independent Publishing.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
TEMBOK API DAN RANGKAIAN PERSENDIRIAN MAYA - TSS 3713
FIREWALLS AND VIRTUAL PRIVATE NETWORKS - TSS 3713
3 Credit Hours
Prerequisite: None
Course Synopsis
In this course, students will learn how to define and describe network firewall security
procedures. This includes demonstration on how to configure firewall interfaces, protocols and
attack guards to protect networks from security threats that may arrive via Internet traffic.
Students will also learn about security policy design and management, secure router design,
installation, configuration and maintenance, and Virtual Private Network (VPN) implementation
using routers and firewalls.
Course Outcomes
At the end of this course, students are able to:
1. Identify, plan and design appropriate network security policies.
2. Understand the concept, types and architecture of firewalls and VPNs.
3. Design and implement basic firewall and VPN configuration.
4. Read and analyse firewalls and VPN logs.
References
1. Northcutt, S., Zeltser, L., Winters, S., Kent, K. & Ritchey, R.W. (2005). Inside Network
Perimeter Security. Second Edition. Carmel: Sams Publishing.
2. Ousley, M.R., Bragg, R. & Strassberg, K. (2003). Network Security: The Complete
Reference. New York: McGraw-Hill Osborne Media.
3. Stewart, J.M. (2010). Network Security, Firewalls and VPNs. Boston: Jones & Bartlett
Learning.
4. Tibbs, R. & Oakes, E. (2005). Firewall and VPNs: Principles and Practices. New Jersey:
Prentice Hall.
5. Whitman, M.E., Mattord, H.J. & Green, A. (2012). Guide to Firewalls and VPNs. Third
Edition. Kentucky: Delmar Cengage Learning.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PENGATURCARAAN DEFENSIVE - TSS 3733
DEFENSIVE PROGRAMMING - TSS 3733
3 Credit Hours
Prerequisite: None
Course Synopsis
This course introduces the principles and practices of defensive programming. Defensive
programming means writing programs in a safe fashion, to avoid vulnerabilities that can be
exploited by attackers. 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 Outcomes
At 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.
4. Apply their knowledge to the construction of secure software systems effectively.
References
1. Edmunds, B. (2016). Securing PHP Apps. New York: Apress.
2. OWASP Secure Coding Practices Quick Reference Guide.
3. Gordan, A. (2015). Official (ISC)2 Guide to the CISSP CBK. Fourth Edition. Florida:
(ISC)2 Press.
4. Stuttard, D., Pinto, M. (2011). The Web Application Hacker's Handbook: Discovering and
Exploiting Security Flaws. Indianapolis: Wiley Publishing, Inc.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SISTEM PENGESAN PENCEROBOHAN RANGKAIAN - TSS 3743
NETWORK INTRUSION DETECTION SYSTEM - TSS 3743
3 Credit Hours
Prerequisite: None
Course Synopsis
In 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 Outcomes
At the end of this course, students are able to:
1. Understand the concept of network intrusion.
2. Understand the components of Network Intrusion Detection System.
3. Design and implement Network Intrusion Detection System.
4. Understand the methodology of network scanning and attacks.
5. Conduct logging and audit on network using Network Intrusion Detection System.
References
1. 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.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SIJIL DIGITAL DAN INFRASTRUKTUR KUNCI AWAM - TSS 3753
DIGITAL CERTIFICATES AND PUBLIC KEY INFRASTRUCTURE - TSS 3753
3 Credit Hours
Prerequisite: None
Course Synopsis
This course will introduce students to Digital Certificates and Public Key Infrastructure (PKI).
Students will learn how to install and use digital certificates in browsers and e-mails, and
discuss Grids Modernisation Initiative (GMI) and Kerberized Certificate Authority (KCA). The
course provides essential knowledge and skills needed to select, design and deploy PKI to
secure existing and future applications within organisations.
Course Outcomes
At the end of this course, students are able to:
1. Understand the concept of Digital Certificates and Public Key Infrastructure (PKI).
2. Apply the concept of Digital Certificates and Public Key Infrastructure (PKI).
3. Understand the Malaysia Cyberlaw and Digital Signature Act 1997.
4. Design secure e-mail system using Digital Certificates and Public Key Infrastructure
(PKI).
5. Implement secure web server using Digital Certificates and Public Key Infrastructure
(PKI).
References
1. Krutz, R.L. & Vines, R.D. (2007). The CISSP Prep Guide: Mastering CISSP and CAP.
Hoboken, NJ: John Wiley & Sons.
2. Malaysia Cyberlaw. (1997). Digital Signature Act.
3. Piper, F.C. & Murphy, S. (2002). Cryptography: A Very Short Introduction. Oxford:
Oxford University Press.
4. Stallings, W. (2011) Cryptography and Network Security: Principles and Practice. Fifth
Edition. Boston: Prentice Hall.
5. Viega, J, Messier, M. & Chandra, P. (2002). Network Security with OpenSSL.
Sebastopol, CA: O'Reilly.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
JABATAN
SAINS
PERTAHANAN
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
JABATAN SAINS PERTAHANAN
Profesor M.Sc.
Prof. Emeritus Dato’ Dr. Wan Md Zin bin Wan Yunus,
B.Sc. (Chemistry)(UKM), Postgraduate Dip. (Chemistry)(Kelsterton College, UK),
(Analytical Chemistry)(Salford, UK), Ph.D. (Analytical Chemistry)(Salford, UK), D.P.S.K.
Prof. Dr. Abdul Ghapor bin Hussin
B.Sc. (Hons.)(Mathematical Sciences)(UM), M.Sc. (Statistics)(Leeds, UK), Ph.D. (Statistics)
(Sheffield, UK)
Lt. Kol. Prof. Ts. Dr. Muhd Zuazhan bin Yahya
B.Sc. (Hons.)(Physics with Education)(UM), M.Sc. (Solid State Ionics)(UM), Ph.D. (Advanced
Materials)(UM)
Pensyarah Kanan
Ts Dr. Syarifah Bahiyah Rahayu binti Syed Mansoor
BScBA in Computer Information System (NAU, US), Master of Information Tech. (QUT, AU),
Ph.D. Information Science (UKM)
Dr. Sharifah Aishah binti Syed Ali
B.Sc. (Mathematical Sciences) (UIAM), M.Sc. (Mathematics)(UKM), Ph.D. (Mngmnt. Sc.)
(Strathclyde, UK)
Ts Dr. Fazilatulaili binti Ali
B.Sc. (Mathematics) (UKM), M.Sc. (Management Mathematics) (UKM), Ph.D. (Civil Eng. -
Transport Operation Research) (Newcastle, UK)
Ts. Dr. Nur Diyana binti Kamarudin
B.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)
Dr. Ruzanna binti Mat Jusoh
B.Sc. (Mathematics) (UKM), M.Sc. (Management Mathematics) (UKM), Ph.D. (Civil
Engineering) (Glasgow, UK)
Puan Khairani binti Abd. Majid
A.A. (Mathematics)(Dutchess Community College, USA), B.Sc. (Statistics)(SUNY, USA), M.Sc.
(Operational Research)(New Haven, USA)
Dr. Mohd. Iqbal Bin Shamsudheen
B.Sc. Hons. (Statistics)(UM), M.Sc. (Statistics) (UPNM), Ph.D. (Statistical Science) (University
College London, UK)
Ts. Dr. Mohd. Sidek Fadhil bin Mohd. Yunus
Diploma of Computer Network and System (UniKL-MIIT), B. IT. Hons. Computer System
Security (UniKL-MIIT), M.Sc. Computer Science (UPNM), Ph.D. Computer Science (UPNM)
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
Pensyarah
Encik Ahmad Shafiq Abdul Rahman
Bachelor of Mathematical Science (Hons) (UIAM), Master of Science (Teaching of Mathematics)
(USM)
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
OBJEKTIF DAN HASIL PEMBELAJARAN
PROGRAM 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 1 Menghasilkan 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 2 Melahirkan 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 3 Melahirkan 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.
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PEO 4 BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
Melahirkan 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.
Hasil Pembelajaran Program
Programme Learning Outcomes (PLO)
PLO 1 Graduan dapat mengaplikasikan pengetahuan dalam bidang
PLO 2 Penyelidikan Operasi dengan Sains Data di pangkalan tentera, agensi
PLO 3 dan industri lain.
PLO 4
Graduates are able to apply knowledge in the field of Operations
Research with Data Science at military bases, agencies and other
industries.
Graduan 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.
Graduan 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.
Graduan dapat mengaplikasikan kemahiran bekerja berkumpulan
untuk mencapai matlamat yang sama.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PLO 5 Graduates are able to apply working skills as an individual or as a team
to achieve common goals.
PLO 6 Graduan dapat berkomunikasi secara efektif dalam bidang
PLO 7 Penyelidikan Operasi dengan Sains Data di peringkat nasional dan
PLO 8 antarabangsa.
PLO 9
PLO 10 Graduates are able to communicate effectively in the field of Operations
Research with Data Science at national and international levels.
Graduan 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.
Graduan mempunyai ciri-ciri kepimpinan intelektual yang berkarakter.
Graduates possess the attributes of intellectual leaders of characters.
Graduan 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.
Graduan dapat menggunakan kemahiran pengurusan dan
keusahawanan.
Graduates are able to apply management and entrepreneurial skills.
Graduan memiliki sikap positif, beretika, profesional dan peka
terhadap alam sekitar.
Graduates possess positive attitude, ethical, professional and sensitive
to the environment.
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
STRUKTUR KURSUS DAN JUMLAH KREDIT KEPERLUAN PROGRAM
SARJANA 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 KREDIT
Kursus Universiti: 24 (+2)
i. Kursus Teras Universiti 6
ii. Kursus Elektif Universiti
45
Kursus Teras Program: 18
i. Teras Penyelidikan Operasi 9
ii. Teras Sains Data
iii. Teras Bersama Penyelidikan Operasi dengan Sains Data 12
12
Kursus Elektif Program: 126 (+2)
i. Elektif Penyelidikan Operasi
ii. Elektif Sains Data
JUMLAH KREDIT UNTUK BERGRADUAT
KURSUS TERAS PROGRAM
SARJANA MUDA PENYELIDIKAN OPERASI DENGAN SAINS DATA (ZC33)
KURSUS TERAS PENYELIDIKAN OPERASI
Kursus-kursus Teras Penyelidikan Operasi adalah wajib diambil oleh semua pelajar Sarjana
Muda Penyelidikan Operasi dengan Sains Data seperti berikut:
KOD KURSUS NAMA KURSUS KREDIT
TPQ 3313 Calculus 3
TPQ 3363 Linear Algebra 3
TPQ 3403 Regression Analysis 3
TPQ 3323 Design of Experiments 3
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
TPQ 3373 Linear and Integer Programming 3
TPQ 3423 Statistical Computing with R 3
TPQ 3413 Simulation and Queuing Theory 3
TPQ 3393 Numerical Analysis 3
TPQ 3332 Final Year Project I 2
TPQ 3383 Network Flow 3
TPQ 3344 Final Year Project II 4
TPQ 3612 Industrial Training 12
45
JUMLAH KREDIT
KURSUS TERAS SAINS DATA
Kursus-kursus Teras Sains Data adalah wajib diambil oleh semua pelajar Sarjana Muda
Penyelidikan Operasi dengan Sains Data seperti berikut:
KOD KURSUS NAMA KURSUS KREDIT
TPD 3343 Discrete Mathematics 3
TPD 3363 Object-oriented Programming 3
TPD 3323 Data Structures 3
TPD 3333 Database Systems 3
TPD 3373 Systems Analysis and Design 3
TPD 3353 Introduction to Data Analytics 3
18
JUMLAH KREDIT
KURSUS TERAS BERSAMA PENYELIDIKAN OPERASI DENGAN SAINS DATA
Kursus-kursus Teras Bersama Penyelidikan Operasi dengan Sains Data adalah wajib diambil
oleh semua pelajar Sarjana Muda Penyelidikan Operasi dengan Sains Data seperti berikut:
KOD KURSUS NAMA KURSUS KREDIT
TPQ 3353 Introduction to Programming 3
TPQ 3433 Statistics for Operations Research 3
TPD 3313 Artificial Intelligence 3
9
JUMLAH KREDIT
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BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
KURSUS ELEKTIF PROGRAM
SARJANA MUDA PENYELIDIKAN OPERASI DENGAN SAINS DATA (ZC33)
KURSUS ELEKTIF PENYELIDIKAN OPERASI
Bagi kursus Elektif Program Penyelidikan Operasi, pelajar perlu memilih sebanyak 12 kredit
sahaja. Kursus-kursus Elektif Program Penyelidikan Operasi adalah seperti berikut:
KOD KURSUS NAMA KURSUS KREDIT
TPQ 3783 Statistical Inference 3
TPQ 3733 Econometrics 3
TPQ 3793 Time Series and Forecasting 3
TPQ 3753 Game Theory 3
TPQ 3713 Decision Analysis 3
TPQ 3773 Project Management 3
TPQ 3743 Efficiency and Productivity Analysis 3
TPQ 3763 Introduction to Materials Management 3
KURSUS ELEKTIF SAINS DATA
Bagi kursus Elektif Program Sains Data, pelajar perlu memilih sebanyak 12 kredit sahaja.
Kursus-kursus Elektif Program Sains Data adalah seperti berikut:
KOD KURSUS NAMA KURSUS KREDIT
TPD 3713 Machine Learning for Data Science 3
TPD 3723 Web Programming and Development 3
TPD 3733 Computer Networks and Security 3
TPD 3743 Computer Graphics and Visualisation 3
TPD 3753 Big Data Analytics and Development 3
TPD 3783 Introduction to Cryptography 3
TPD 3763 Introduction to IoT Data Analysis 3
TPD 3773 Image Processing and Analytics 3
190
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
STRUKTUR KURIKULUM
PROGRAM SARJANA MUDA PENYELIDIKAN OPERASI DENGAN SAINS DATA (ZC33)
TAHUN PERTAMA
KOD SEMESTER 1 KOD NAMA SEMESTER 2 NAMA
KURSUS NAMA KURSUS KURSUS KURSUS KURSUS
Military Leadership MPU 3132 KOD KURSUS
DUM 3022 2 2
2 MPU 3212 Appreciation of Ethics
MPU 3142 Philosophy and and Civilizations 2
Currents Issues 2 Basic
MPU 3412 / Entrepreneurship 1
MPU 3422 Human Movement 1
Science / LLF 3XX1 Foreign Language II (+1)
Community Service (+1) 3
LLF 3XX1 Foreign Language I 3 LLA 3XX1 Foreign Language II 3
TPQ 3433 (Audit)
LLA 3XX1 Foreign Language I 3 Statistics for 3
(Audit) 3 TPQ 3373 Operations Research 3
3 Linear and Integer 2
TPQ 3313 Calculus 1 TPD 3363 Programming 1
1 TPD 3333 Object-oriented 1
TPQ 3353 Introduction to ALK 3112* Programming
TPQ 3363 Programming 2 PLS 3121** *20
*20/22 QKA 3121*** Database Systems **19
Linear Algebra **21/23 ***19
***21/23 Latihan Ketenteraan
TPD 3343 Discrete Mathematics Umum*
PLS 3111** PALAPES 1* PALAPES 2**
QKA 3111*** Kesatria Al-Fateh 1***
Kesatria Al-Fateh 2***
Basic Grammar and
LLE 3042**** Vocabulary****
JUMLAH KREDIT JUMLAH KREDIT
KOD KURSUS SEMESTER PENDEK KREDIT
NAMA KURSUS 3
3
TPQ 3323 Design of Experiments 3
9
TPQ 3393 Numerical Analysis
TPD 3373 Systems Analysis and Design
JUMLAH KREDIT
191
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
TAHUN KEDUA
KOD SEMESTER 3 KOD NAMA SEMESTER 4 NAMA
KURSUS KURSUS KURSUS KURSUS
DUS 3022 NAMA KURSUS DUS 3012 KOD KURSUS
2 DUS 3032 2
LLE 3012 Introduction to 2 Military History 2
Strategic Studies LLE 3032
MPU 3312 / English for Academic 2 Military Law and Law 2
MPU 3332 / Writing of Armed Conflict
MPU 3342 Nationhood in World 3
Politics / 3 Al-Ghazali’s
TPQ 3413 Fiqh Keutamaan/ Dialogue: English
Integrity and Anti - Communication
Corruption
Simulation and TPQ 3403 Regression Analysis 3
Queuing Theory TPD 3353 3
TPQ 3XX3 Introduction to Data 3
TPQ 3383 Network Flow Analytics
TPD 3323 Data Structures 3 Elective 1 (OR)
TPD 3XX3 Elective 1 (DS) 3 TPD 3XX3 Elective 2 (DS) 3
ALK 3122* 2
Latihan Ketenteraan TPD 3313 Artificial Intelligence 3
Umum* 1
2 2
PLS 3131** PALAPES 3** QKS 3172* Tempur Tanpa
*20 Senjata* 1
QKA 3132*** Kesatria Al-Fateh 3*** **19 PLS 3141** 2
***20 QXX YYY2*** PALAPES 4** *23
**22
Kokurikulum*** ***23
JUMLAH KREDIT JUMLAH KREDIT
KOD KURSUS SEMESTER PENDEK KREDIT
NAMA KURSUS 2
3
TPQ 3332 Final Year Project I 3
TPQ 3423 Statistical Computing with R 8
TPD 3XX3 Elective 3 (DS)
JUMLAH KREDIT
192
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
TAHUN KETIGA
SEMESTER 5 SEMESTER 6
NAMA KURSUS
KOD Final Year Project II KREDIT KOD NAMA KURSUS KREDIT
KURSUS 4 KURSUS 12
TPQ 3344 TPQ 3612 Industrial Training 12
TPQ 37X3 Elective 2 (OR) 3
TPQ 37X3 Elective 3 (OR) 3
TPQ 37X3 Elective 4 (OR) 3
TPD 3XX3 Elective 4 (DS) 3 JUMLAH KREDIT
PLS 3151** PALAPES 5**
PLS 3161** PALAPES 6** 1
JUMLAH KREDIT 1
*16
**18
***16
Nota:
* Diambil oleh Pegawai Kadet
** Diambil oleh Pelajar PALAPES
*** Diambil oleh Pelajar Awam
**** Diambil oleh pelajar yang mendapat Band 1 dan 2 dalam peperiksaan MUET
193
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SINOPSIS KURSUS TERAS PENYELIDIKAN OPERASI
KALKULUS – TMM 3313
CALCULUS – TMM 3313
3 Credit Hours
Prerequisite: None
Course Synopsis
This 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 Outcomes
At the end of this course, students are able to:
1. Use the properties of function, limit and continuity, technique of differentiation and
integration.
2. Compute limit, continuity, derivatives and integrals.
3. Solve problems involving differentation and integration.
4. Sketch the graph of a polynomial or rational function using the technique learned in
calculus.
References
1. Stewart, J. (2020). 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.
194
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
ALJABAR LINEAR – TMM 3333
LINEAR ALGEBRA – TMM 3333
3 Credit Hours
Prerequisite: None
Course Synopsis
Linear 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 Outcomes
At the end of this course, students are able to:
1. Identify the basic properties of matrices including determinants, inverse matrices,
eigenvalues, eigenvectors, and linear transformations.
2. Solve systems of linear equations.
3. Comprehend vector spaces and product spaces.
4. Apply concepts of Linear Algebra to solve a variety of practical problems.
References
1. 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.
5. Friedberg, S.H., Insel, A.J. & Spence, L.E. (2003). Linear Algebra. Featured Titles for
Linear Algebra (Advanced) Series. Fourth Edition. Essex: Pearson Education Limited.
195
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
ANALISIS REGRESI – TMS 3423
REGRESSION ANALYSIS – TMS 3423
3 Credit Hours
Prerequisite: STATISTICS FOR OPERATIONS RESEARCH - TMS 3413
Course Synopsis
The 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 Outcomes
At the end of this course, students are able to:
1. Define the concepts of simple and multiple linear regression.
2. Interpret the understanding of Variance-Bias decomposition and variable selection.
3. Apply the regression concepts on logistics, generalise linear and nonparametric
regression.
4. Analyse the parametric and nonparametric regression.
References
1. Montgomery, D. C., Peck, E. A. & Vining, G. (2021). Introduction to Linear Regression
Analysis. 6th Edition. Wiley Series in Probability and Statistics.
2. Wasserman, L. (2010). All of Statistics: A Concise Course in Statistical Inference. New
York: Springer.
3. Navidi, W. (2015). Statistics for Engineers & Scientists. Fourth Edition. New York:
McGraw-Hill Education.
4. Seber, G.A.F. & Lee, A.J. (2003). Linear Regression Analysis. Second Edition. Hoboken,
New Jersey: John Wiley & Sons, Inc.
5. Montgomery, D.C., Peck, E.A. & Vining, G.E. (2012). Introduction to Linear Regression
Analysis. Fifth Edition. Hoboken, New Jersey: John Wiley & Sons, Inc.
6. Mendenhall, W. & Sincich, T. (2011). A Second Course in Statistics: Regression
Analysis. Seventh Edition. London: Pearson Education Limited.
196
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
REKA BENTUK EKSPERIMEN – TMS 3433
DESIGN OF EXPERIMENTS – TMS 3433
3 Credit Hours
Prerequisite: None
Course Synopsis
This course deals with the concepts and techniques used in the design and analysis of
experiments. The concepts and different models of an experimental design will be studied,
leading to hands-on experience by applying various techniques on scientific researches. Topics
covered will include an introduction to experiments, completely randomised designs, blocking
designs, cofounding and fractional designs with two levels.
Course Outcomes
At the end of this course, students are able to:
1. Describe the concepts of experimental design.
2. Determine the key factor in the process.
3. Apply the concepts in creating a designed experiment including randomisation, blocking
and replication.
4. Design and complete their own scientific experiment and interpret statistical results from
an experiment and report them in non-technical language.
References
1. Montgomery, D.C. (2012). Design and Analysis of Experiments. Eighth Edition. New
York: John Wiley & Sons, Inc.
2. Kuehl, R.O. (1999). Design and Analysis of Experiments: Statistical Principles of
Research Design and Analysis. Second Edition. Pacific Grove, California: Duxbury
Press.
3. Montgomery, D.C. (2008). Design and Analysis of Experiments. Seventh Edition. New
York: John Wiley & Sons, Inc.
4. Montgomery, D.C., Runger, G.C. & Hubele, N.F. (2011). Engineering Statistics. Fifth
Edition. Hoboken, New Jersey: John Wiley & Sons, Ltd.
5. Oehlert, G.W. (2000). A First Course in Design and Analysis of Experiments. New York:
W.H. Freeman & Company.
197
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PENGATURCARAAN LINEAR DAN INTEGER – TMQ 3513
LINEAR AND INTEGER PROGRAMMING – TMQ 3513
3 Credit Hours
Prerequisite: LINEAR ALGEBRA - TMM 3333
Course Synopsis
Linear programming (LP) is a fundamental technique for solving optimisation problems. This
course introduces the theory and the solution of linear programming problems including linear
programming, the simplex method, duality, theory of integer linear programming models and
methods and several special types of linear programming problems such as transportation,
assignment, and network models. Students will also learn how to use the computer software
such as Microsoft Excel to solve and analyse problems.
Course Outcomes
At the end of this course, students are able to:
1. Identify objective function, decision variables, and constraints correctly.
2. Apply their knowledge with the basic notions and techniques to develop operational
research models from the verbal description of the real system.
3. Analyse the mathematical models using fundamental methods in optimisation and
computer software.
References
1. Taha, H.A. (2017). Operations Research: An Introduction. Tenth Edition. Upper Saddle
River, New Jersey: Pearson.
2. 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.
3. Winston, W.L. (2004). Operations Research: Applications and Algorithms. Fourth
Edition. Boston: Cengage Learning.
4. Wolsey, L.A. (1998). Integer Programming. New York: John Wiley & Sons, Inc.
5. Gupta, P.K. & Hira, D.S. (2007). Operations Research. Twenty-Second Edition. New
Delhi: S. Chand & Company Ltd.
198
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
TEORI PERMAINAN DAN ANALISIS KEPUTUSAN DALAM PEPERANGAN – TMQ 3523
GAME THEORY AND DECISION ANALYSIS IN WARFARE – TMQ 3523
3 Credit Hours
Prerequisite: None
Course Synopsis
This 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 Outcomes
At the end of this course, students are able to:
1. Apply various knowledge in game theory and decision making.
2. Analyse decision making techniques under certainties and uncertainties.
3. Evaluate the tools of decision analysis for a strategic decision by combining
mathematical strategies with intuitive decision making.
4. Construct the solutions for decision and game theory problem.
References
1. 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.
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FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
7. Clement, R.T. (1997). Making Hard Decisions: An Introduction to Decision Analysis.
Second Edition. Belmont, California: Duxbury Press.
200
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SIMULASI DAN GILIRAN – TMQ 3533
SIMULATION AND QUEUING – TMQ 3533
3 Credit Hours
Prerequisite: None
Course Synopsis
This 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 Outcomes
At the end of this course, students are able to:
1. Recognise the main analytical techniques used in queuing systems.
2. Differentiate the basic issues and methodologies used in modelling real problems.
3. Apply statistical aspects of simulation and queuing systems.
4. Use simulation software for the solution of queuing models.
References
1. John F. Shortle, James M. Thompson, Donald Gross, Carl M. Harris. Fundamentals of
Queueing Theory (2018). 5th Edition. Wiley Series in Probability and Statistics.
2. Bhat, U. Narayan, (2015). An Introductory to Queueing Theory. Birkhauser.
3. Law, A.M. (2014). Simulation Modeling and Analysis. Fifth Edition. New York: McGraw-
Hill Education.
4. Rossetti, M.D. (2016). Simulation Modeling and Arena. Second Edition. Hoboken, New
Jersey: John Wiley and Sons, Inc.
5. Robinson, S. (2014). The Practice of Model Development and Use. Second Edition.
London: Red Globe Press.
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.
7. Pidd, M. (1998). Computer Simulation in Management Science. Fourth Edition. New
York: John Wiley & Sons, Inc.
201
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
LOGISTIK PERTAHANAN DAN PENGURUSAN PENGANGKUTAN – TMQ 3543
DEFENCE LOGISTICS AND TRANSPORTATION MANAGEMENT – TMQ 3543
3 Credit Hours
Prerequisite: None
Course Synopsis
This 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 Outcomes
At the end of this course, students are able to:
1. State the importance of logistics and transportation management.
2. Apply theoretical knowledge to practical issues in logistics and transportation
management.
3. Evaluate knowledge/science within the area of logistics and transportation management.
References
1. 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.
202
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FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PROJEK TAHUN AKHIR I – TMQ 3552
FINAL YEAR PROJECT I – TMQ 3552
2 Credit Hours
Prerequisite: None
Course Synopsis
This course aims to enable students to combine learnings from their previous courses of
Operations Research with Data Science to develop a research or project proposal. The
research proposal should comprise of problem statement, research aim, research objectives,
scope and limitations of research, the significance of research, literature review and appropriate
research methodology. The research methodology covers research designs (quantitative and
mixed methods) and research methods which provide a detailed procedure or technique of data
collection and analysis. Students will be required to obtain feedback from the supervisors about
the research or project proposal. At the end of the project, students should hand in a written
report and conduct a presentation.
Course Outcomes
At the end of this course, students are able to:
1. Identify the research topic related to operational research.
2. Construct the problem statement according to a selected topic.
3. Produce the research objectives following the problem statement.
4. Conduct a literature review on the research topic.
5. Explain a research methodology to achieve the objectives of research.
References
1. Zulkifly Mat Radzi (2009). Panduan Menulis Tesis Fakulti Sains dan Teknologi
Pertahanan. Kuala Lumpur: Universiti Pertahanan Nasional Malaysia.
2. Punch, K.F. (2006). Developing Effective Research Proposals. Second Edition. London:
SAGE Publication Ltd.
3. Ogden, T.E. & Goldberg, I.A. (2002). Research Proposals: A Guide to Success. Third
Edition. California: Academic Press.
4. Wisker, G. (2009). The Undergraduate Research Handbook. Basingstoke: Palgrave
Macmillan Ltd.
5. O’Leary, Z. (2018). Little Quick Fix: Research Proposal. London: Sage.
203
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FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
ALIRAN RANGKAIAN – TMQ 3563
NETWORK FLOW – TMQ 3563
3 Credit Hours
Prerequisite: None
Course Synopsis
This course is an undergraduate level course 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. The purpose of this course is to present students with current
theory and applications of network flow problems. It allows them to model and solve real
systems, analyse and develop algorithms. This course will survey the underlying concepts of
graph and network problems, theory, solution methods, algorithm complexity, and various
applications.
Course Outcomes
At the end of this course, students are able to:
1. Identify the use of Network Flow in optimisation problems.
2. Formulate network models using proper procedure and techniques.
3. Analyse each network models using proper network algorithm and choosing the one
which generates the best solution.
4. Propose the best solution to be considered in solving optimisation problems.
References
1. Taha, H.A. (2017). Operations Research: An Introduction. Tenth Edition. Boston, United
States: Pearson.
2. Lucas, M.W. (2010). Network Flow Analysis. New York: No Starch Press Inc.
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.
5. Adli Mustafa (1991). Aliran Rangkaian. Pulau Pinang, Malaysia: Penerbit Universiti
Sains Malaysia.
204
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PROJEK TAHUN AKHIR II – TMQ 3574
FINAL YEAR PROJECT II – TMQ 3574
4 Credit Hours
Prerequisite: FINAL YEAR PROJECT I - TMQ 3552
Course Synopsis
This course is a continuation of Final Year Project I. Students should be able to enhance their
knowledge in analysing collected data, developing a mathematical model/solution approach,
testing a model/solution approach using the data obtained/simulated data, analysing and
discussing the results, and providing recommendations or suggestions for the problems stated
in the research proposal. At the end of the semester, the students are required to submit a final
year project report and to conduct a project presentation.
Course Outcomes
At the end of this course, students are able to:
1. Analyse the collected data using tools as stated in the research proposal.
2. Develop and test an appropriate mathematical model or solution approach.
3. Produce discussions and conclusion based on the research findings.
4. Explain the research findings during the presentation.
References
1. Fakulti Sains dan Teknologi Pertahanan. (2009). Panduan Penulisan Tesis dan Projek
Penyelidikan Sarjana Muda. Kuala Lumpur: Universiti Pertahanan Nasional Malaysia.
2. Mohd Hazali Mohamed Halip, Nor Asiakin Hasbullah & Syahaneim Marzukhi. (2011).
Panduan Penulisan Tesis dan Projek Sarjana Muda Sains Komputer. Kuala Lumpur:
Jabatan Sains Komputer, Universiti Pertahanan Nasional Malaysia.
3. Zarina Shukur. (2007). Menulis Dokumen Projek Perisian untuk Prasiswazah. Selangor:
Kumpulan Sains Pengaturcaraan, UKM.
4. Berndtsson, M., Hansson, J. Olsson, B. & Lundell, B. (2004). Planning and Implementing
your Final Year Project – With Success! : A Guide for Students in Computer Science and
Information Systems. London: Springer.
5. Grätzer, G. (2014). Practical LaTeX. Switzerland: Springer.
205
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
LATIHAN INDUSTRI – TMQ 3612
INDUSTRIAL TRAINING – TMQ 3612
12 Credit Hours
Prerequisite: COMPLETED YEAR TWO
Course Synopsis
This course provides an opportunity for students to enhance their knowledge and skills in
various actual working environments. From the industrial training, the students will be able to
gain the knowledge through hands-on observation and job execution as well as to improve soft
skills such as communication, critical thinking, teamwork, work ethics, and leadership. The
students are required to undergo industrial training for one semester attached to operations
research with data science related organisation. Upon completion of the training, the students
shall submit a report on the work done in the organisation to the faculty.
Course Outcomes
At the end of this course, students are able to:
1. Apply the knowledge that they have learned in the workplace.
2. Acquire basic professional skills by experiencing a real working environment.
3. Produce Industrial Training report after the training is completed.
References
1. Halip, M.H.M., Awang, N.F., Yahaya, Y.H., Khairuddin, M.A. & Ghani, S.A. (2011).
Panduan Latihan Industri Sarjana Muda Sains Komputer. Kuala Lumpur: Jabatan Sains
Komputer, Universiti Pertahanan Nasional Malaysia.
206
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SINOPSIS KURSUS TERAS SAINS DATA
MATEMATIK DISKRET - TSJ 3213
DISCRETE MATHEMATICS - TSJ 3213
3 Credit Hours
Prerequisite: None
Course Synopsis
This course introduces the mathematical concepts of discrete mathematics in computer science
and how to use them in practice. It covers the fundamental topics such as propositional and
predicate logic, fundamental principles of counting, set theory, mathematical induction,
functions, and relations.
Course Outcomes
At the end of this course, students are able to:
1. Explain truth tables, the concepts of logical equivalence and logical implication, and the
use of quantifiers.
2. Apply basic counting principles to solve a variety of problems.
3. Solve problems which involve discrete data structures such as sets, relations and
discrete functions.
4. Apply mathematical induction in proving a mathematical statement.
References
1. Fortney, J.P. (2021). Discrete Mathematics for Computer Science: An Example-based
Introduction. First Edition. Boca Raton: CRC Press.
2. Epp, S.S. (2020). Discrete Mathematics with Applications. Fifth Edition. Boston:
Cengage Learning, Inc.
3. Johnsonbaugh, R. (2019). Discrete Mathematics. Eighth Edition. New Jersey: Pearson
Education Limited.
4. Rosen, K.H. (2018). Discrete Mathematics and Its Applications. Eighth Edition. New
York: McGraw-Hill Education.
5. Grimaldi, R. (2014). Discrete and Combinatorial Mathematics: An Applied Introduction.
Fifth Edition. Essex: Pearson Education Limited.
6. Stein, C., Drysdale, R.L. & Bogart, K. (2011). Discrete Mathematics for Computer
Scientists. Massachusetts: Pearson Education, Inc.
7. Malik, D.S. & Sen, M.K. (2010). Discrete Mathematics: Theory and Applications. Revised
Edition. Singapore: Cengage Learning Asia Pte. Ltd.
207
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PENGATURCARAAN BERORIENTASIKAN OBJEK - TSP 3223
OBJECT-ORIENTED PROGRAMMING - TSP 3223
3 Credit Hours
Prerequisite: None
Course Synopsis
This course gives an understanding of basic concepts in object-oriented programming (including
the concept of object and class), and illustrates them using Java language. The course also
provides a deeper understanding of object-oriented programme design and implementation and
more advanced features of object-orientation, such as inheritance, polymorphism, abstract
classes, exceptions, recursion, searching and sorting.
Course Outcomes
At the end of this course, students are able to:
1. Understand and identify basic concepts of a high level programming language using
object-oriented programming correctly.
2. Demonstrate and apply their knowledge with the basic notions and techniques to
develop the algorithm and basic object-oriented programming language.
3. Analyse a simple object-oriented programming problem specification.
4. Design and write a programme which maps to object-oriented programming correctly
and effectively.
References
1. Gaddis, Y. (2010). Starting Out with Java: From Control Structures Through Objects.
Fourth Edition. New York: Addison-Wesley.
2. Liang, Y.D. (2011). Introduction to JAVA Programming. Ninth Edition. New Jersey:
Prentice Hall.
3. Malik, D.S. (2012). Java Programming: From Problem Analysis to Program Design. Fifth
Edition. New York: Course Technology Cengage Learning.
4. Sufian Idris, Marini Abu Bakar & Norleyza Jailani. (2002). OO Java: Pengaturcaraan
Berorientasikan Objek Menggunakan Java. Kuala Lumpur: Prentice Hall.
5. Thomas, C.W. (2010). An Introduction to Object-Oriented Programming with Java. Fifth
Edition. New York: McGraw-Hill.
208
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
STRUKTUR DATA - TSP 3233
DATA STRUCTURES - TSP 3233
3 Credit Hours
Prerequisite: None
Course Synopsis
This course covers the fundamentals of data structures and algorithms applied in programming
solutions to application problems. It is a study of the design, implementation, and use of data
abstraction including linked structures, lists, stacks, queues, trees and graph structures.
Course Outcomes
At the end of this course, students are able to:
1. Understand the theory and concept of data structures.
2. Design and develop a program using C programming language.
3. Identify, diagnose and resolve real world problems.
4. Understand standard methods and tools in developing and managing computer-based
system.
References
1. Gilberg, R.F. & Forouzan, B.H. (2004). Data Structures: A Pseudocode Approach with C.
Second Edition. Washington: Thomson Course Technology.
2. Liang, Y.D. (2007). Introduction to Programming With C++: Comprehensive Version.
New Jersey: Pearson International Edition.
3. Malik, D.S. (2010). C++ Programming: Program Design Including Data Structures. Fifth
Edition. Washington: Course Technology.
4. Malik, D.S. (2011). Data Structures Using C++. Second Edition. Washington: Course
Technology.
5. Weiss, M.A. (2006). Data Structures and Algorithm Analysis in C++. Third Edition.
Boston: Addison-Wesley.
209
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SISTEM PANGKALAN DATA - TST 3223
DATABASE SYSTEMS - TST 3223
3 Credit Hours
Prerequisite: None
Course Synopsis
The course covers database systems and file systems, relational data modeling, SQL, E-R
modeling, normalisation, transaction and concurrency control, database design and
implementation. Latest and future trends in database will be discussed. A database
management system will be introduced and used for practical sessions.
Course Outcomes
At the end of this course, students are able to:
1. Understand database management concept and user requirements.
2. Develop and refine the conceptual data model, including all entities, relationships,
attributes, and apply normalisation techniques.
3. Design and implement database applications.
References
1. Coronel, C., Rob, P. & Morris, S. (2009). Database Systems: Design, Implementation,
and Management. Ninth Edition. New York: Thomson Learning.
2. Connolly, T.M. & Begg, C.E. (2015). Database Systems: A Practical Approach to
Design, Implementation and Management. Sixth Edition. Essex: Pearson.
3. Molina, H.G., Ullman, J.D. & Widom, J. (2008). Database Systems: The Complete Book.
Second Edition. New York: Prentice Hall.
4. Elmasri, R. & Navathe, S.B. (2010). Fundamental of Database Systems. Sixth Edition.
New York: Addison-Wesley.
5. Date, C.J. (2003). An Introduction to Database System. Eighth Edition. Singapore:
Addison-Wesley.
210
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
ANALISIS DAN REKA BENTUK SISTEM - TST 3253
SYSTEMS ANALYSIS AND DESIGN - TST 3253
3 Credit Hours
Prerequisite: None
Course Synopsis
This course introduces the fundamentals of information systems analysis and design by
covering a variety of current methods, tools, and techniques. The course will cover system
development activities in the context of when they typically occur. Most of the course will be
devoted to learning and practising the techniques and processes used by the systems analysts
at each phase within the systems development cycle and to working as a team to create a
system solution for a client.
Course Outcomes
At the end of this course, students are able to:
1. Undertake investigations and perform a feasibility analysis.
2. Interpret qualitative and quantitative information.
3. Undertake a system modeling analysis and write a software requirement specification
according to accepted standards.
4. Identify requirements, necessary entities and their relationships, through a process of
system development.
5. Apply the systems analysis and design process for the group project.
References
1. Dennis, A., Wixom, B.H. & Roth, R.M. (2008). Systems Analysis and Design. Fourth
Edition. Boston: Wiley.
2. Satzinger, J.W., Jackson, R.B. & Burd, S.D. (2006). Systems Analysis and Design in a
Changing World. Fourth Edition. Boston: Course Technology.
3. Shelly, G.B. & Rosenblatt, H.J. (2011). Systems Analysis and Design. Ninth Edition.
Boston: Course Technology.
4. Whitten, J.L. & Bentley, L.D. (2005). Systems Analysis and Design Methods. Seventh
Edition. New York: McGraw-Hill/Irwin.
5. Whitten, J.L. & Bentley, L.D. (2006). Introduction to Systems Analysis and Design. New
York: McGraw-Hill/Irwin.
211
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PENGENALAN KEPADA ANALITIK DATA – TSV 3313
INTRODUCTION TO DATA ANALYTICS – TSV 3313
3 Credit Hours
Prerequisite: None
Course Synopsis
Introduction to Data Analytics 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 Outcomes
At the end of this course, students are able to:
1. Describe and identify basic concepts of a knowledge discovery process, data pre-
preprocessing and processing techniques for data analytics.
2. Apply data preprocessing steps, and data analytics techniques.
3. Develop visualisation of the generated results.
4. Examine the data analytics model and output values correctly.
References
1. Runkler, T.A. (2012). Data Analytics: Models and Algorithms for Intelligent Data
Analysis, Germany: Vieweg+Teubner Verlag Springer.
2. Miller, T.W. (2006). Modeling Techniques in Predictive Analytics: Business Problems
and Solutions with R, Revised and Expanded Edition. New Jersey: Pearson FT Press.
3. Tan, P.N., Steinbach, M. & Kumar, V. (2006). Introduction to Data Mining. England:
Pearson Addison Wesley.
4. Cerrito, P.B. (2006). Introduction to Data Mining Using SAS Enterprise Miner. Cary, NC:
SAS Institute Inc
212
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SINOPSIS KURSUS TERAS BERSAMA
ASAS KEPADA PENGATURCARAAN – TMM 3323
INTRODUCTION TO PROGRAMMING – TMM 3323
3 Credit Hours
Prerequisite: None
Course Synopsis
This 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 Outcomes
At the end of this course, students are able to:
1. Identify basic concepts of a high-level programming language using Java correctly.
2. Apply their knowledge with the basic notions and techniques to develop the algorithm
and basic Java programming language.
3. Analyse a simple Java programming problem specification.
4. Design a program which maps to Java programming correctly and effectively.
References
1. 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.
213
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
STATISTIK BAGI PENYELIDIKAN OPERASI – TMS 3413
STATISTICS FOR OPERATIONS RESEARCH – TMS 3413
3 Credit Hours
Prerequisite: None
Course Synopsis
This course covers topics from types of data, describing data sets graphically and numerically,
commonly used probability distribution, sampling distribution and confidence interval, hypothesis
testing, analysis of variance, goodness of fit and contingency table and finally regression and
correlation. This course will focus more on the procedures of the analysis and interpretation of
results. Software application is strongly emphasised.
Course Outcomes
At the end of this course, students are able to:
1. Identify the theoretical foundations of basic concepts in probability and statistics.
2. Apply the theoretical foundations of the knowledge to model pertaining to related
problems.
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.
References
1. Navidi, W. (2015). Statistics for Engineers and Scientists. Fourth Edition. New York:
McGraw-Hill Education.
2. Bluman, A.G. (2018). Elementary Statistics. Tenth Edition. New York: McGraw-Hill
Education.
3. Reid, H.M. (2014). Introduction to Statistics: Fundamental Concepts and Procedures of
Data Analysis. New York: Sage.
4. Bluman, A.G. (2013). Elementary Statistics. Sixth Edition. New York: McGraw-Hill
Education.
5. Montgomery, D.C., Runger, G.C. & Hubele, N.F. (2011). Engineering Statistics. Fifth
Edition. Hoboken, New Jersey: John Wiley & Sons, Inc.
214
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
KECERDASAN BUATAN - TST 3273
ARTIFICIAL INTELLIGENCE - TST 3273
3 Credit Hours
Prerequisite: None
Course Synopsis
The course aims to introduce the principle, methods and techniques used in artificial intelligence
systems. It exposes students to search techniques, logic, knowledge representation and
reasoning, intelligent agents, and machine learning. Programming languages such as Prolog
and MATLAB will be used in the course in practical sessions and coursework.
Course Outcomes
At the end of this course, students are able to:
1. Understand concepts and techniques of artificial intelligence through the study of
important artificial intelligence techniques.
2. Gain theoretical knowledge to be able to reason about the behaviour of Artificial
Intelligence system.
3. Apply artificial intelligence concept to an intelligent system application.
References
1. Bratko, I. (2000). PROLOG Programming for Artificial Intelligence. Third Edition.
Singapore: Addison-Wesley.
2. Luger, G.F. (2009). Artificial Intelligence: Structures and Strategies for Complex Problem
Solving. Sixth Edition. Boston: Pearson Education.
3. Padhy, N.P. (2007). Artificial Intelligence and Intelligent Systems. New York: Oxford
University Press.
4. Nilsson, N.J. (1998). Artificial Intelligence: A New Synthesis. New York: Morgan
Kaufmann Publishers.
5. Russell, S.J. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Third
Edition. New Jersey: Prentice Hall.
215
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SINOPSIS KURSUS ELEKTIF PENYELIDIKAN OPERASI
ASAS KEPADA KRIPTOGRAFI – TMM 3713
INTRODUCTION TO CRYPTOGRAPHY – TMM 3713
3 Credit Hours
Prerequisite: None
Course Synopsis
This course provides an introduction to the theory of public key cryptography and to the
mathematical ideas underlying that theory. The topics that will be covered are evolution of
cryptography, number theory, information theory, asymmetric cryptography and message
authentication among the many facets of modern cryptography. This course concentrates
primarily on public key cryptosystems and digital signature schemes. Students should develop
an understanding of Diffie–Hellman key exchange, discrete logarithm-based cryptosystem, the
RSA cryptosystem, primality testing, factorisation algorithm and digital signatures.
Course Outcomes
At the end of this course, students are able to:
1. Describe the terminologies used in cryptography and the basic model of public key
cryptosystems and digital signature schemes.
2. Perform encryption and decryption processes for classical and public key cryptosystems.
3. Apply cryptographic techniques in supporting the security requirements of a system.
References
1. Hoffstein, J., Pipher, J. & Silverman, J.H. (2014). An Introduction to Mathematical
Cryptography. Second Edition. New York: Springer.
2. Smart, N.P. (2003). Cryptography: An Introduction. Third Edition. New York: McGraw-Hill
Education.
3. Schneier, B. (1996). Applied Cryptography. Second Edition. New York: John Wiley &
Sons, Inc.
216
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
ANALISIS NUMERIK – TMM 3723
NUMERICAL ANALYSIS – TMM 3723
3 Credit Hours
Prerequisite: None
Course Synopsis
This 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 Outcomes
At the end of this course, students are able to:
1. Identify the most common numerical methods used in operations research and
engineering.
2. Assess the efficiency of a selected numerical method when more than one option is
available to solve a certain class of problem.
3. Demonstrate the convergence properties, limitation of different numerical methods and
implement using MATLAB’s programming language.
References
1. 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.
217
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
INFERENS STATISTIK – TMS 3713
STATISTICAL INFERENCE – TMS 3713
3 Credit Hours
Prerequisite: None
Course Synopsis
This course focuses on the fundamentals of statistical inference for population parameters
based on a general decision theoretic framework, which covers estimation and hypothesis
testing. Concepts, methods, and theory are emphasised.
Course Outcomes
At the end of this course, students are able to:
1. Apply the concept of probability in relation and concept of sampling error.
2. Explain the concept of confidence interval for the mean.
3. Construct the null hypothesis and the alternative hypothesis.
4. Investigate the purpose of a priori and post hoc statistical power analysis.
References
1. Hogg, R.V., Tanis, E.A. & Zimmerman, D.L. (2015). Probability and Statistical Inference.
Ninth Edition. Essex: Pearson.
2. Miller, M. & Miller, I. (2012). John E. Freund's Mathematical Statistics with Applications.
Eight Edition. Essex: Pearson.
3. Casella, G. & Berger, R. (2001). Statistical Inference. Second Edition. Kentucky:
Duxbury Press.
4. Silvey, S.D. (2003). Statistical Inference. New York: Chapman & Hall/CRC.
5. Mukhopadhyay, N. (2006). Introductory Statistical Inference. New York: Chapman &
Hall/CRC.
218
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
EKONOMETRIK – TMS 3723
ECONOMETRICS – TMS 3723
3 Credit Hours
Prerequisite: None
Course Synopsis
This course will introduce the students to the theory and applications of econometric analysis.
The course has a strong applied emphasis. This is an empirically-based course in which
students will develop hypotheses grounded in theory and then test those hypotheses using data
and statistical methods. The first part of the course provides an introduction to basic
econometric concepts and data analysis techniques, such as descriptive statistics, correlation
and regression, probability, chance variability, and sampling. The second part of the course
covers hypothesis testing, the basic regression theory and techniques used in empirical work
which include simple and multiple regression models, dummy variables, heteroskedasticity, and
methods of instrumental variables.
Course Outcomes
At the end of this course, students are able to:
1. Determine the correct statistical technique to test hypotheses for different types of
variables.
2. Identify problems in data analysis and interpretation arising from methodological or data
problems.
3. Develop econometrically testable hypotheses based upon economic theory.
4. Demonstrate application of appropriate econometric methods to test an economic
theory.
References
1. Wooldridge, J.M. (2016). Introductory Econometrics: A Modern Approach. Sixth Edition.
Boston: Cengage Learning.
2. Hayashi, F. (2000). Econometrics. New Jersey: Princeton University Press.
3. Baltagi, B.H. (2011). Econometrics. Fifth Edition. Berlin, Heidelberg: Springer.
4. Westhoff, F. (2013). An Introduction to Econometrics: A Self-contained Approach.
Cambridge: MIT Press.
5. Stock, J.H. & Watson, M.W. (2015). Introduction to Econometrics. Third Edition. New
York: Pearson.
219
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SIRI MASA DAN RAMALAN – TMS 3733
TIME SERIES AND FORECASTING – TMS 3733
3 Credit Hours
Prerequisite: None
Course Synopsis
This course introduces the art and science of forecasting. Students will be guided on the proper
procedures in the forecasting works. Topics include forecasting techniques, forecasting process,
understanding time series, univariate modelling techniques, econometric modelling, Box-
Jenkins methodology and evaluating the models.
Course Outcomes
At the end of this course, students are able to:
1. Recognise the forecasting method in management.
2. Explain the forecasting techniques and forecasting process.
3. Produce the model by using the univariate modeling technique.
4. Construct the model by using Box-Jenkins methodology.
References
1. Ord, J.K. & Fildes, R. (2013). Principles of Business Forecasting. Mason, Ohio: Cengage
Learning.
2. Hyndman, R.J. & Anthanasopoulos, G. (2018). Forecasting Principles and Practice.
Second Edition. Melbourne: Otexts.
3. Mohd Alias Lazim. (2011). Introductory Business Forecasting: A Practical Approach.
Third Edition. Shah Alam, Selangor: UPENA.
4. Silvia, J., Iqbal, A., Bullard, S., Watt, S. & Swankoski, K. (2014). Economic and Business
Forecasting: Analysing and Interpreting Econometric Results. Hoboken, New Jersey:
John Wiley & Sons, Inc.
5. Brockwell, P.J. & Davis, R.A. (2002). Introduction to Time Series and Forecasting.
Second Edition. New York: Springer-Verlag.
6. Montgomery, D.C., Jennings, C.L. & Kulahci, M. (2008). Introduction to Time Series
Analysis and Forecasting. Hoboken, New Jersey: John Wiley & Sons Inc.
220
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
SINOPSIS KURSUS ELEKTIF SAINS DATA
PERLOMBONGAN DATA - TSI 3323
DATA MINING - TSI 3323
3 Credit Hours
Prerequisite: None
Course Synopsis
The 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 Outcomes
At the end of this course, students are able to:
1. Describe and identify basic concepts of a knowledge discovery process, data
preprocessing and data mining techniques.
2. Explain and summarise data preprocessing steps, and data mining techniques.
3. Apply the data mining techniques for a given data set.
4. Analyse the data mining model and output values correctly.
References
1. Han, J. & Kamber, M. (2011). Data Mining: Concepts and Techniques. Third Edition.
USA: 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. USA:
Addison-Wesley.
221
BUKU PANDUAN AKADEMIK SARJANA MUDA SESI AKADEMIK 2022/2023
FAKULTI SAINS DAN TEKNOLOGI PERTAHANAN
PENGATURCARAAN DAN PEMBANGUNAN WEB - TSP 3243
WEB PROGRAMMING AND DEVELOPMENT - TSP 3243
3 Credit Hours
Prerequisite: None
Course Synopsis
The course introduces concept, design and development of web-based application. Markup
languages and tools such as HTML, CSS, XML, JavaScript and PHP will be introduced for the
development of interactive and dynamic web-based application in client-server technology. This
course requires the student to build multiple web pages and implement at least one major
website design that interacts with a database.
Course Outcomes
At the end of this course, students are able to:
1. Build knowledge and experience using client-side technologies of the World Wide Web
including HTML, DHTML, XHTML, CSS, JavaScript and XML.
2. Understand and use basic SQL (Standard Query Language) with PHP server side
scripting language to handle data to and from database.
3. Demonstrate and apply their knowledge with the basic notions and techniques to
develop an e-commerce website and online information system project.
4. Design and develop an online system that integrates the PHP server side scripting with
MySQL database for the development of their online system.
References
1. Nixon, R. (2009). Learning PHP, MySQL, and JavaScript. New York: O'Reilly Media.
2. Matthews, M. & Cronan, J. (2009). Dynamic Web Programming: A Beginner's Guide.
New York: McGraw-Hill Professional.
3. Quigley, E. & Gargenta, M. (2006). PHP and MySQL by Example. Boston: Prentice Hall.
4. Ullman, L.E. (2011). PHP and MySQL for Dynamic Web Sites. Fourth Edition. Berkeley:
Peach Pit Press.
5. Welling, L. & Thomson, L. (2003). PHP and MySQL Web Development. Second Edition.
New York: Sam's Publishing.
222