Predicting Student Performance Through Data Mining: A Case Study in Sultan Ageng Tirtayasa University
*Department of Electrical Engineering, Faculty of Engineering, Universitas Sultan Ageng Tirtayasa
Jl. Jend. Sudirman KM 3 Kota Bumi, Cilegon, Banten 42435, Indonesia
**Research Center for Data and Information Science, National Research and Innovation Agency
Jl. M.H. Thamrin No.8, Jakarta Pusat, Jakarta 10340, Indonesia
Failure in compulsory subjects such as chemistry, calculus, physics, and basic control systems could hamper the graduation process of students. Thus, students must be successful in such obligatory courses. To address this issue, this study aims to predict student performance based on their learning outcomes using data mining techniques. In particular, we utilize decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms to predict student performance. The data for this study were gathered from the learning outcomes of students in the basic control systems course and subsequently modeled using binary and nine-level classifications. The experimental results showed that DT could perform better than KNN, SVM, and NB in the binary and nine-level classifications. Interestingly, the results of DT (i.e., the prediction values) are almost similar to those of the original values of the basic control systems course.
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