Penentuan Penerima Bantuan Program Keluarga Harapan menggunakan Algoritma Random Forest di Desa Kebonrejo - Putra, Krisna Giana (Universitas Pahlawan Tuanku Tambusai, 2024)
This study aims to improve the effectiveness of the distribution of Family Hope Program (PKH) assistance in Mronjo Village by applying the Random Forest algorithm. The manual method used previously was often ineffective and prone to errors. This study uses a quantitative method with a descriptive approach. Data were collected through observation, interviews, and literature studies, with a sample of 99 people from a population of 838 residents of Kebunrejo Hamlet, who were selected using a purposive sampling technique. The results showed that the Random Forest algorithm was able to classify PKH recipients well. Important factors that influence classification include income, employment, household conditions, chronic diseases, and suitability status. Model evaluation using the Confusion Matrix showed that the model had high accuracy, precision, recall, and F1 value, each reaching 90%. Although most of the predictions were in accordance with the original data, there were still some classification errors that needed to be corrected. Overall, the Random Forest model has proven effective in determining PKH assistance recipients and can be a reference for improving social assistance distribution policies in the future.