Nurzeatul Hamimah Abdul Hamid

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



Research Interests: Software Creation and Management, Computer systems and computational processes, Artificial Intelligence, Data Structures and Algorithms


Nurzeatul Hamimah Abdul Hamid is a senior lecturer of Information System in Universiti Teknologi Mara. She received a master’s degree in Intelligent Systems at the University of Sussex, UK in 2005. She teaches courses related to fundamentals of artificial intelligence, artificial intelligence programming paradigm and intelligent agent. Her primary research interests involve the software agents, normative multi-agent systems, trust and reputation systems.

Author Articles
A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

By Syaidatus Syahira Ahmad Tarmizi Sofianita Mutalib Nurzeatul Hamimah Abdul Hamid Shuzlina Abdul Rahman

DOI:, Pub. Date: 8 Aug. 2019

Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students’ information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE.

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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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