COMPARISON OF RANDOM FOREST AND SUPPORT VECTOR MACHINE CLASSIFICATION METHODS FOR PREDICTING THE ACCURACY LEVEL OF MADRASAH DATA - Syarip, Dodi Irawan; Notodiputro, Khairil Anwar; Sartono, Bagus (MEDIA STATISTIKA, 2025)
This study aims to identify the most effective classification method for predicting the accuracy level of madrasah data with class imbalance. Two machine learning approaches were employed: Random Forest (RF) and Support Vector Machine (SVM). Based on the AUC values, it was concluded that the RF model had a slightly better performance in predicting the accuracy level of the madrasah data, with an average AUC of 62.82, compared to the SVM model, which had an average AUC of 62.33. Among all models, the highest and consistent performance was achieved by the RF model using ROSE techniques. The results of measuring variable importance showed that the predictor variables with the greatest influence in predicting the accuracy level of the madrasah data are the number of students and the student-to-teacher and staff ratio. This finding suggests that school principals and madrasah administrative staff should prioritize ensuring the completeness of student, teacher, and staff data to improve the overall reliability of madrasah data.