Implementation of the Extreme Gradient Boosting(XGBoost) Method in the Classification of Recipients of Habitable Housing Rehabilitation in Central Aceh Regency

Ira Zulfa
Hendri Syahputra
Fitranuddin Fitranuddin
Adellia Divandariga S

International Journal of Electrical Engineering, Mathematics and Computer Science
Vol. 2 No. 2 (2025): International Journal of Electrical Engineering, Mathematics and Computer Scien

In Central Aceh Regency, many households still live in uninhabitable conditions. The government is running a program to rehabilitate habitable houses, but the selection of recipients is still done manually, causing inefficiency and inconsistency. This study implements the Extreme Gradient Boosting (XGBoost) algorithm to classify aid recipients automatically and accurately. Using a machine learning approach, data is collected based on variables of structural conditions, building materials, ventilation, lighting, and sanitation. Hyperparameter tuning is performed to optimize model performance. The implementation results show that XGBoost is able to support fair, efficient, and transparent decision making in housing assistance programs.