Integrasi Explainable AI (SHAP) Pada Model Machine Learning Untuk Analisis Faktor Penentu Kualitas Fasilitas Kesehatan Berbasis Web

Abstract

The quality and equitable distribution of healthcare facilities are vital indicators of regional public service development. Although modern Machine Learning models such as Extreme Gradient Boosting (XGBoost) achieve exceptionally high predictive performance in classifying medical facility quality levels, their black-box nature often limits decision-making transparency for policymakers. This study aims to integrate an Explainable Artificial Intelligence (XAI) approach using SHapley Additive exPlanations (SHAP) into an XGBoost model developed in Google Colab and deployed as an interactive web dashboard via Streamlit. The dataset comprises aggregated BPJS Healthcare Facility data across Indonesian regencies/cities supplemented with operational indicators. The experimental results demonstrate superior modeling performance, achieving an accuracy of 95.15% and an F1-Score of 97.30%. Through SHAP Summary Plot analysis, Skor_Fasilitas_UGD and Total_Faskes were identified as the primary dominant factors driving high-quality facility classifications. The Streamlit web application successfully visualizes individual feature contributions (SHAP Waterfall Plot) in real-time, providing intuitive clinical and managerial transparency for public health planning