Optimasi Random Forest dan Support Vector Machine untuk Prediksi Kelulusan Seleksi Mahasiswa Baru
Keywords:
admission prediction, Grid Search, Random Forest, Support Vector Machine (SVM), student selectionAbstract
New student admission requires a reliable analytical mechanism because the characteristics of applicants and selection indicators differ across admission tracks. This study compares Random Forest and Support Vector Machine and evaluates Grid Search hyperparameter optimization for predicting admission outcomes at Politeknik STMI Jakarta. The dataset contains 6,338 applicant records from 2022–2025, divided into Achievement, Test, Joint, and UTBK tracks. Data were cleaned, transformed, normalized with MinMaxScaler, split using a stratified 80:20 scheme, and tuned through five-fold cross-validation. Performance was measured using accuracy, precision, recall, F1-score, and confusion matrix. Random Forest delivered the most consistent results, with accuracies of 92.75%, 93.04%, 80.56%, and 90.39% across the four tracks. Grid Search improved Random Forest accuracy by 1.27 percentage points on the Test track and 3.71 points on the Joint track, while no improvement occurred on the Achievement and UTBK tracks. The findings support track-specific model selection and position Random Forest as a decision-support tool, while class imbalance and label quality remain important implementation constraints.
Keywords: admission prediction; Grid Search; Random Forest; Support Vector Machine; student selection
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