PERBANDINGAN ALGORITMA XGBOOST DAN RANDOM FOREST DENGAN TEKNIK FEATURE ENGINEERING PADA KLASIFIKASI
Keywords:
Algoritma XGBoost, Random Forest, Feature Engineering, Klasifikasi KelulusanAbstract
Penelitian ini bertujuan membandingkan kinerja algoritma machine learning XGBoost dan Random Forest dengan teknik feature engineering untuk klasifikasi kelulusan siswa di SMK Putra Anda Binjai. Proses penentuan kelulusan secara manual sering memakan waktu, tenaga, dan rentan terhadap kesalahan, sehingga diperlukan solusi berbasis data yang efektif. Penelitian ini menggunakan data 500 siswa kelas XII tahun ajaran 2023/2024, yang meliputi nilai rata-rata rapor, nilai Ujian Kompetensi Keahlian (ASBK), dan persentase kehadiran. Setelah melalui tahapan preprocessing dan feature engineering, kedua model dilatih dan dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil evaluasi model manual menunjukkan akurasi 90% untuk kedua algoritma. Sementara itu, pada implementasi program, Random Forest mencatat performa sempurna dengan akurasi, presisi, recall, dan F1-score 100%, sedangkan XGBoost juga menunjukkan kinerja sangat baik dengan akurasi 99.8%. Hasil ini membuktikan kedua algoritma ini sangat efektif untuk klasifikasi kelulusan siswa.
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