Model Prediksi Pelunasan Haji Berbasis XGBoost Dengan Interpretasi Shap: Studi Prediksi Pelunasan Haji dengan XGBoost dan SHAP di Provinsi Jambi - Yan Apriadi; Dodo Zaenal Abidin; Jasmir Jasmir (Asosiasi Riset Ilmu Teknik Indonesia, 2025)
This study develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled