Estimasi Indeks Pembangunan Manusia Tingkat Kecamatan Menggunakan Data Geospasial Remote Sensing, Point of Interest, dan Statistik Resmi dengan Metode Machine Learning

Samuel Marvelius Sianturi
Bony Parulian Josaphat

Prosiding Seminar Nasional Official Statistics
Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026

The limited data coverage for measuring the Human Development Index (IPM), like National Socio-Economic Survey (Susenas) causes the Statistics Indonesia (BPS) only publishes the IPM at the district/city level. Meanwhile, assessing the quality of human development would be more effective if the IPM were available at a smaller area, such as the sub-district. This study examines the use of geospatial data and official statistics to estimate the IPM at the sub-district level. The results indicate that the best machine learning model is Random Forest Regression with Root Mean Square Error (RMSE) evaluation value of 3.7817, Mean Absolute Error (MAE) of 2.6509, and Mean Absolute Percentage Error (MAPE) of 4.01%. The estimation results show that sub-districts with high estimated HDI are consentrated in areas with high population densities and better accessibility to health, education, and economic facilities, such as sub-districts in Kupang City.