Pengembangan Model Prediksi Kelulusan Calon Mahasiswa Sarjana pada Sistem Seleksi SNMPTN IPB

Abstrak

Since 2019, the SNMPTN selection process at IPB has used web-based selection media and specific algorithms. However, the process has not yet implemented machine learning-based modeling that can provide recommendations on a student's likelihood of being accepted as an IPB student. This study aims to find out what factors influence prospective students passing the IPB SNMPTN pathway and to develop machine learning modeling using Random Forest and Binary Logistic Regression. Four models were built and trained using hyperparameter tuning. The first model uses all features without balancing. The second model uses all features and SMOTE. The third model uses feature selection and SMOTE, and the fourth uses feature selection by Expert Adjustment (EA) and SMOTE. The results show that the models tested using test data with SMOTE data balancing consistently show higher recall values compared to models without data balancing. The third model with Binary Logistic Regression on West Java data and the second model with Binary Logistic Regression on Non-West Java data show the best recall values of 88.93% and 86.91%, respectively. The modeling results also show that the order of college selection, school index category, academic achievements, and program of study choice significantly impact the prediction of applicants’ passing.