Ariff Md Ab Malik

Work place: Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor



Research Interests: Database Management System, Information Systems, Information Security, Systems Architecture, Computer systems and computational processes


Ariff Md Ab Malik is a senior lecturer of Faculty of Business Management. He is currently acting as the Head of Strategic Planning (Information Management) of UiTM. He received his PHD from University Kebangsaan Malaysia in Computer Science. His primary research interest involves Information System Management, Heuristic, and Benchmarking.

Author Articles
Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

By Nurafifah Mohammad Suhaimi Shuzlina Abdul Rahman Sofianita Mutalib Nurzeatul Hamimah Abdul Hamid Ariff Md Ab Malik

DOI:, Pub. Date: 8 Jul. 2019

Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students’ achievement, university’s performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students’ academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students’ graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naïve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students’ graduation time.

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