Data mining is one of the most popular techniques to analyse and predict students’ performance. Hence, this study evaluated two data mining approaches of decision tree and neural network in finding the prediction of students’ academic performance. The data used in this study were collected through a self-administered questionnaire distributed to the students. A comparison was made between the two data mining algorithms. Results proved that the decision tree provided the highest accuracy value in the model. Furthermore, it was found that previous certificate results, gender, status of working part-time, time management, parental marital status, study skills, and preparation before attending lectures were significant factors that affected students’ performance. Based on the prediction, it can be concluded that the students will get a good result in the next semester with a cumulative grade point average (CGPA) of 3.00 and above if the students know how to manage time, use the right study skills, and do some preparation before the lecture begins.
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In-Text Citation: (Husin et al., 2022)
To Cite this Article: Husin, W. Z. W., Zain, M. N. M., Zahan, N. A. N., Adam, P. N. A., & Ab. Aziz, N. (2022). Performance of Decision Tree and Neural Network Approach in Predicting Students’ Performance. International Journal of Academic Research in Business and Social Sciences. 12(6), 1215– 1227.
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