PENERAPAN ALGORITMA RANDOM FOREST UNTUK MENENTUKAN KELAYAKAN PENERIMA BLT: IMPLEMENTATION OF THE RANDOM FOREST ALGORITHM FOR DETERMINING BLT RECIPIENTS' ELIGIBILITY - Muhammad Iqbal; Dahlan Abdullah; Yesy Afrillia (LPPM Universitas Abdurrab, 2025)
Indonesia faces significant challenges regarding poverty, particularly among low-income communities. The government has introduced various programs, including the Direct Cash Aid Program (BLT), to alleviate poverty. However, identifying eligible recipients in Bandar Dua Subdistrict, Pidie Jaya, remains complex and time-consuming. This research aims to implement the Random Forest algorithm to determine the eligibility of cash aid recipients in Bandar Dua, Pidie Jaya, providing accurate and efficient classification based on predefined criteria. The study focuses on BLT recipients in Bandar Dua, comprising 45 villages, using data from 2020 to 2022. The Random Forest algorithm is applied, considering criteria such as income, housing conditions, and the number of dependents. The expected outcomes include valuable insights for the local government in determining the eligibility of cash aid recipients, a classified dataset using the Random Forest algorithm, and a reference for future research. According to the test results using the Random Forest algorithm, an accuracy of 83.33%, precision of 89.04%, and recall of 91.55% were achieved.