Eligibility Analysis of Non-Cash Food Assistance Recipients in Rajadesa Village Using the K-Means Clustering Method - Ati Sumiati; Rini Astuti; Willy Prihartono (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
The distribution of Non-Cash Food Assistance (BPNT) in Rajadesa Village often encounters challenges in accurately determining recipient eligibility. The selection process lacks objectivity due to limited data and suboptimal verification, leading to uneven distribution. To address this issue, the study employs the K-Means Clustering algorithm to enhance the efficiency of BPNT recipient selection. The assessment is based on attributes such as ID number (NIK), name, address, occupation, and the amount of assistance received.The methodology adopts the Knowledge Discovery in Databases (KDD) approach, involving stages such as data selection, preprocessing, transformation, data mining, and evaluation. Data processing is carried out using RapidMiner version 10.5. The clustering results are evaluated using the Davies Bouldin Index (DBI), yielding the best model with three clusters and a DBI value of 0.346. This approach successfully identifies recipient groups more accurately, enabling a more targeted distribution of food assistance. Consequently, the study provides significant contributions to ensuring the eligibility of BPNT recipients in Rajadesa Village through the application of K-Means Clustering-based analytical technology.