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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ariff Md Ab Malik
Universiti Teknologi MARA
Computer Science & IT Engineering

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Predictive analytics of university student intake using supervised methods Muhammad Yunus Iqbal Basheer; Sofianita Mutalib; Nurzeatul Hamimah Abdul Hamid; Shuzlina Abdul-Rahman; Ariff Md Ab Malik
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (368.053 KB) | DOI: 10.11591/ijai.v8.i4.pp367-374

Abstract

Predictive analytics extract important factors and patterns from historical data to predict future outcomes. This paper presents predictive analytics of university student intake using supervised methods. Every year, universities face a lot of academic offer rejection by the applicants. Hence, this research aims to predict student acceptance and rejection towards academic offer given by a university using supervised methods subject to past student intake data. To solve this problem, a lot of past studies had been reviewed starting from nineties era till now. From the analysis, two algorithms had been selected namely Decision Tree and k Nearest Neighbor. The dataset of past student intake was obtained with fifteen attributes, which are applicants’ gender, applicants studied stream during Sijil Peperiksaan Malaysia (SPM), university campuses, applicants’ hometown, disability, campus visit, course choice order in application form, applicant’s six SPM subjects result, orphan and status of acceptance. Several experiments were implemented to find the best model to predict the student’s offer acceptance by evaluating the model accuracy. Both models yield best accuracy at 66 percent with the selected attributes. This research gives a huge impact in selecting which applicants is suitable to be offered as well as adapting the university’s academic offering process in much intelligence way in the future.
Co-Authors Muhammad Yunus Iqbal Basheer Nurzeatul Hamimah Abdul Hamid Shuzlina Abdul-Rahman Sofianita Mutalib