PREDICTIVE MODELLING OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING APPROACHES - A CASE STUDY IN UNIVERSITI ISLAM PAHANG SULTAN AHMAD SHAH

Aug 5, 2026

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Analisis ramalan menggunakan pembelajaran mesin untuk meramal prestasi akademik pelajar di pengajian tinggi. Kajian ini memfokus pembangunan model paling tepat dengan membandingkan teknik seperti regresi logistik multinomial, pokok keputusan, hutan rawak, jiran terdekat K, Naive Bayes, dan mesin vektor sokongan. Faktor utama dikenal pasti melalui Korelasi Cramer's V dan Pekali Korelasi Pangkat Spearman, manakala prestasi dinilai menggunakan ketelitian, pengingatan, ketepatan, skor F1 dan AUC. Berdasarkan data pelajar kursus Statistik Perniagaan (2013–2022), markah terkumpul adalah faktor paling berpengaruh. Pokok keputusan mencapai ketepatan 0.60 dan skor yang lebih tinggi dalam pengingatan serta F1 berbanding model lain.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages85

Reading Time3 h 34 min

AccessFree to download

Abstrak

Pengenalan dan isu keciciran

Kaedah dan teknik pembelajaran mesin

Kaedah analisis faktor

Reka bentuk penilaian prestasi model

Dapatan utama dan model terbaik

PREDICTIVEMODELLING OF STUDENTACADEMIC PERFORMANCE USINGMACHINE LEARNING APPROACHES:A CASE STUDY IN UNIVERSITI ISLAMPAHANG SULTAN AHMAD SHAH NURUL HABIBAH BINTI ABDUL RAHMAN PAHANG AL-SUL MASTER OF SCIENCE UNIVERSITI MALAYSIA PAHANGAL-SULTAN ABDULLAH UNIVERSITI MALAYSIA PAHANG AL-SULTANABDULLAH | DECLARATION OF THESIS AND COPYRIGHT
Author's Full Name : NURUL HABIBAH BINTI ABDUL RAHMAN
Date of Birth
: 15TH FEBRUARY 1985
: PREDICTIVEMODELLING OF STUDENT ACADEMIC
Title
PERFORMANCE USING MACHINE LEARNING
APPROACHES:A CASE STUDY IN UNIVERSITI
ISLAM PAHANG SULTAN AHMAD SHAH
1 : SEMESTERⅡ2023/2024
Academic Session
I declare that this thesis is classified as:
CONFIDENTIAL (Contains confidential information under the Official Secret
□
Act 1997)*
(Contains restricted information as specified by the
□
RESTRICTED
organization where research was done)*
区 OPEN ACCESS
I agree that my thesis to be published as online open access
(Full Text)
I acknowledge that Universiti Malaysia Pahang Al-Sultan Abdullah reserves the following
rights:
The Thesis is the Property of Universiti Malaysia Pahang Al-Sultan Abdullah
1.
The Library of Universiti Malaysia Pahang Al-Sultan Abdullah has the right to
2.
make copies of the thesis for the purpose of research only.
The Library has the right to make copies of the thesis for academic exchange.
3.
(Superyisdr's Signature)
(Student's Signature)
Sahimel Azwal bin Sulaiman
Name of Supervisor
Date:3 July 2024
Date:03/07/2024
—1
AL-SULTAN ABDULLAH | | --- | NOTE:*If the thesis is CONFIDENTIAL or RESTRICTED,please attach a thesis declarationletter. # SUPERVISOR’S DECLARATION I/We*hereby declare that I/We*have checked this thesis/project*and in my/our*opinion,this thesis/project*is adequate in terms of scope and quality for the award of thedegree of*Doctor of Philosophy/Master of Science. Full Name SAHMELAZWAL BIN SULAIMAN Position:SENIOR LECTURER Date :1/7/2024 UMPSA (Co-supe sor's ignature) ULLAH Full Name :NOK AZUANA RAMLI Position:SENIOR LECTURER Date :1/7/2024 # STUDENT'S DECLARATION I hereby declare that the work in this thesis is based on my original work except forquotations and citations which have been duly acknowledged.I also declare that it hasnot been previously or concurrently submitted for any other degree at Universiti MalaysiaPahang Al-Sultan Abdullah or any other institutions. — (Student's Signature) Full Name :NURULHABIBAH BINTI ABDUL RAHMAN ID Number :MSS21002 Date :1/7/2024 心e u山Li之e L # PREDICTIVE MODELLING OF STUDENT ACADEMIC PERFORMANCEUSING MACHINE LEARNING APPROACHES:A CASE STUDY IN UNIVERSITI ISLAM PAHANG SULTAN AHMAD SHAH NURULHABIBAH BINTI ABDUL RAHMAN Thesis submitted in fulfillment of the requirements for the award of the degree ofVERSITIMaster of Science Centre for Mathematical Sciences UNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH JULY 2024 # ACKNOWLEDGEMENTS By the name of Allah,the Most Gracious and the Most Merciful.All praise to Allah forgranting me the chance and fortitude to pursue and accomplish my research proposal formy master in Statistics. First and foremost,I would like to thank my supervisor,Dr.Sahimel Azwal bin Sulaiman,and my co-supervisor,Dr.Nor Azuana binti Ramli,for providing guidelines andreviewing all of my inadequate submissions with patience.It was a privilege for me towork under such brilliant supervisors who assisted me throughout my studies. Very special thanks to my beloved husband,Mohd Shahid bin Mohamed Fathil and myparents for their support and prayers during my studies.Without their will,it would nothave been easy for me to complete my studies. Last but not least,I would like to convey my appreciation to my son,Muhammad ZiyadIman bin Mohd Shahid and all friends who have

FAQs

Apakah sasaran utama kajian ini?

Membangunkan model ramalan yang paling tepat bagi meramal tahap prestasi pelajar menggunakan teknik pembelajaran mesin.

Teknik pembelajaran mesin apakah yang dibandingkan dalam kajian ini?

Regresi logistik multinomial, pokok keputusan, hutan rawak, jiran terdekat K, Naive Bayes, dan mesin vektor sokongan.

Bagaimanakah faktor yang mempengaruhi tahap prestasi pelajar dikenal pasti?

Menggunakan Korelasi Cramer's V dan Pekali Korelasi Pangkat Spearman untuk menentukan faktor yang paling berpengaruh.

Aug 5, 2026

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