COMPARATIVE STUDY OF RESAMPLING TECHNIQUES FOR STUDENT PERFORMANCE PREDICTION USING SMOTE-ENN AND ENSEMBLE LEARNING - Ahmad Zainul Fanani; Affandy; Eni Heni Hermaliani; Heru Agus Santoso (JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), 2026)
This study analyzes the effectiveness of resampling techniques and ensemble learning in addressing class imbalance problems in student performance prediction using the xAPI-Edu-Data dataset from the Kalboard 360 LMS. The class imbalance ratio of 1:1.66 leads to bias in traditional classification models toward the majority class. The study evaluates six resampling methods, including hybrid SMOTE-ENN, combined with nine individual classifiers and three ensemble models (bagging, voting, and stacking). Evaluation was conducted using accuracy, precision, recall, and F1-score with stratified 5-fold cross-validation and hyperparameter optimization through GridSearchCV. The results indicate that the combination of SMOTE-ENN with voting and stacking achieved the best performance of 98.18% across all evaluation metrics and significantly improved minority-class recall, demonstrating its effectiveness for developing early warning systems to identify at-risk students.