Abstract
The Self-Help Housing Stimulant Assistance (BSPS) is a government house renovation program aimed at low-income communities. This program aims to enhance self-sufficiency in construction and improve the quality of houses, facilities, infrastructure, and public utilities through the principle of mutual cooperation. BSPS recipients must meet several criteria, such as income, house ownership status, house size, floor type, wall type, roof type, and water source. To address issues based on these criteria, Data Mining techniques using the Naive Bayes method were employed. This study utilized a dataset of BSPS recipients in Air Genting Village, Air Batu Sub-district, comprising 88 samples. The classification results from applying Naive Bayes yielded a precision value of 84%, a recall value of 81%, an F1-score of 82%, and an accuracy of 81%. The objective of this research is to facilitate the classification of eligible and rightful recipients of government assistance in the form of BSPS. The results of this study are expected to provide an alternative solution in determining BSPS recipients in Air Genting Village, Air Batu Sub-district
