Classifying Honors Class Eligibility Using SVM - Khulfani Hendrawan; Ika Arfiani (Indonesian Journal of Data and Science, 2025)
Introduction: The honors class program aims to group outstanding students, but an objective data-based classification system is not yet available. This can result in high-potential students going undetected due to the selection process relying on self-registration and causing a lack of student interest. Method: This research uses the Support Vector Machine (SVM) algorithm to classify the eligibility of students to participate in the honors program based on academic and non-academic data. The dataset consists of 453 entries with an imbalanced class distribution, which was then balanced using the SMOTE technique. The model was trained using GridSearchCV to find the optimal parameters and compared with four types of SVM kernels: linear, polynomial, sigmoid, and radial basis function (RBF). Result: The RBF kernel achieved the best performance with an accuracy of 84%, precision of 0.77, recall of 0.84, and an F1-score of 0.79. However, it was found that the precision in the minority class (high-achieving students) is still lower than in the majority class. Conclusion: The SVM model, particularly with the RBF kernel, has proven effective in automating the classification of students for the honors program. However, further improvements are needed to enhance performance on the minority class to make the selection system fairer and more accurate