PERBANDINGAN ALGORITMA RANDOM FOREST DAN XGBOOST DALAM KLASIFIKASI PENERIMA BANTUAN PANGAN NON-TUNAI (BPNT) DI PROVINSI JAWA BARAT

Yulianti, Riska
Ilmani, Erdanisa Aghnia
Waliulu, Megawati Zein
Sartono, Bagus
Firdawanti, Aulia Rizki

Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik

This study compares the performance of Random Forest and XGBoost algorithms in classifying recipients of Non-Cash Food Assistance (BPNT) in West Java Province. The data used is from the 2023 National Socio-Economic Survey (SUSENAS) comprising 25,890 households, with 23.6% BPNT recipients and 76.4% non-recipients. The study includes data exploration, preprocessing, handling class imbalance, baseline modeling, and hyperparameter tuning using Grid Search. The results indicate that undersampling effectively increases the recall of Random Forest to 80.01% and XGBoost to 74.04%, albeit at the expense of accuracy. The most influential variables in classification include the head of household's employment status, flooring material of the house, and type of land/building ownership proof. These findings support the utilization of data-driven algorithms to enhance the accuracy and fairness of BPNT distribution.