Identifikasi Peubah Penting terhadap Indeks Prestasi Mahasiswa D-IV Jalur SNBP dengan Random Forest dan EasyEnsemble

Abstract

Indeks Prestasi (IP) Semester 1 merupakan indikator keberhasilan akademik awal mahasiswa yang dipengaruhi oleh berbagai faktor. Sementara itu, distribusi kategori IP pada mahasiswa D-IV jalur Seleksi Nasional Berdasarkan Prestasi (SNBP) cenderung tidak seimbang sehingga dapat menurunkan kemampuan model klasifikasi dalam mengenali kelas minoritas. Penelitian ini bertujuan membandingkan performa Random Forest standar dan Random Forest berbasis EasyEnsemble dalam mengklasifikasikan kategori IP Semester 1 serta mengidentifikasi peubah yang berkontribusi terhadap hasil klasifikasi menggunakan SHapley Additive exPlanations (SHAP). Data yang digunakan merupakan data primer mahasiswa D-IV angkatan 62 jalur SNBP IPB University tahun 2025. Evaluasi model dilakukan menggunakan repeated stratified hold out sebanyak lima kali ulangan dengan kombinasi hyperparameter ntree (50, 100, 150) dan mtry (3, 5, 7). Performa model dibandingkan menggunakan Balanced Accuracy, Macro F1-Score, dan Geometric Mean (G-Mean), sedangkan pengaruh hyperparameter dianalisis menggunakan ANOVA faktorial dua arah. Hasil penelitian menunjukkan bahwa Random Forest berbasis EasyEnsemble pada skema tiga kelas menghasilkan performa terbaik dengan peningkatan Balanced Accuracy dan G-Mean dibandingkan Random Forest standar. Analisis SHAP menunjukkan bahwa Rataan Pendidikan Kewarganegaraan, Rataan Bahasa Indonesia, Rataan Matematika, Rataan Prasyarat, dan Rataan Total merupakan peubah yang paling berkontribusi terhadap klasifikasi kategori IP Semester 1.

First-semester Grade Point Average (GPA) is an important indicator of students' early academic performance. However, the distribution of GPA categories among D-IV students admitted through the National Selection Based on Achievement (SNBP) pathway is imbalanced, which may reduce the ability of classification models to identify minority classes. This study compared the performance of the standard Random Forest and EasyEnsemble-based Random Forest models for classifying first-semester GPA categories and identified important variables contributing to the classification using SHapley Additive exPlanations (SHAP). The study used primary data from D-IV students of the 62nd cohort admitted through the SNBP pathway at IPB University in 2025. Model evaluation was conducted using repeated stratified holdout with five repetitions and combinations of ntree (50, 100, and 150) and mtry (3, 5, and 7). Model performance was assessed using Balanced Accuracy, Macro F1-Score, and Geometric Mean (G-Mean), while the effects of hyperparameters were evaluated using two-way ANOVA. The results showed that the EasyEnsemble-based Random Forest with the three-class scheme achieved the best performance by improving Balanced Accuracy and G-Mean compared with the standard Random Forest. SHAP analysis indicated that Civic Education average score, Indonesian Language average score, Mathematics average score, prerequisite subject average score, and overall report card average score were the most influential variables in classifying first-semester GPA categories.