Utilizing Machine Learning For Identifying Potential Beneficiaries of Family Hope Program

In identifying families who are entitled to PKH assistance there are often obstacles such as RTSM identification errors, this is caused by the negligence of officials so that they are not accurate in making confirmations in large numbers. An automated system that can predict RTSM can be a solution to this problem, a system based on a machine learning model. This study aims to analyze the machine learning model Decision Tree C45 (DT C45), K-Nearest Neighbor (KNN), and Naive Bayes (NB). The results showed that Decision Tree C45 was the optimal model to implement with an accuracy value of 70%.