Abstract—Student graduation accuracy is one of the indicators of the success of higher education institutions in carrying out the teaching and learning process and as a component of higher education accreditation. So it is not surprising that building a system that can predict or classify students graduating on time or not on time is necessary for universities to monitor the exact number of students graduating on time using educational technology. Unfortunately, educational technology or machine learning with data mining approaches is less accurate in classifying classes with unbalanced data. Therefore, this research purpose is to build a machine learning system that can improve classification performance on unbalanced class data between students who graduate on time and graduate late. This study applies the Synthetic Minority Oversampling Technique (SMOTE) method to improve the classifying performance of the Support Vector Machine (SVM) data mining method. The results of the study concluded that using the SMOTE method increased the accuracy, precision, and sensitivity of the SVM method in classifying class data of unbalanced student graduation times. The SVM performance score rises by 3% for classification accuracy, 8% for classification precision, and 25% for classification sensitivity.
Index Terms—Classification, educational technology, machine learning, data mining, Support Vector Machine (SVM), Synthetic Minority Oversampling Technique (SMOTE).
Anthony Anggrawan is with Information Technology Education Department, Bumigora University, Indonesia.
Hairani Hairani is with Computer Science Department, Bumigora University, Indonesia (e-mail: firstname.lastname@example.org).
Christofer Satria is with Visual Design Communication, Bumigora University, Indonesia (e-mail: email@example.com).
Cite: Anthony Anggrawan*, Hairani Hairani, and Christofer Satria, "Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE," International Journal of Information and Education Technology vol. 13, no. 2, pp. 289-295, 2023.Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).