Classification of Recipients of the Family Hope Program in West Sumatra Province Using the Random Forest Algorithm

Abstract

According to the Central Statistics Agency (BPS), the percentage of poor people in West Sumatra Province increased by 0.02% in 2024. One of the government's efforts to overcome poverty is a social assistance program issued by the government to help people who are economically disadvantaged. The targeted distribution of social assistance is an important challenge in improving community welfare, especially for families receiving PKH benefits. This study aims to classify households receiving the Family Hope Program (PKH) in West Sumatra Province using a random forest algorithm with Synthetic Minority Oversampling Technique (SMOTE). This study uses data on PKH recipient households in West Sumatra Province in 2024, which has a significant class imbalance. Therefore, the SMOTE method was applied to balance the data. The data was divided into training and testing data with a ratio of 80%:20%, then parameter tuning was performed to optimize mtry and ntree. The model was evaluated using a confusion matrix to compare model performance. The results show that the accuracy obtained is 76%. The precision value is 72%, the recall is 84%, and the f1-score is 78%. Based on the Mean Decrease Gini value, the head of household's diploma became the main attribute in determining whether a household received PKH or not. This study concluded that the use of SMOTE in the random forest algorithm performed well in classifying PKH recipients in West Sumatra Province, where the model performed well and was quite reliable in identifying PKH recipients.