Klasifikasi Penentuan Prioritas Bantuan PKH Dengan Clustering K-Means dan C4.5 Pada Desa Talang Belido
DOI:
https://doi.org/10.33998/jakakom.2026.6.1.2337Keywords:
PKH, Data Mining, K-Means, Clustering, C4.5, Davies Bouldin Index, RapidMinerAbstract
The Family Hope Program (PKH) is a conditional social assistance program provided to Poor Families (KM) who have been designated as beneficiaries. However, the distribution of this assistance still faces challenges in terms of equity and accuracy. The management of large data volumes, time efficiency, and data accuracy are significant challenges in determining the most eligible recipients in Talang Belido Village. To address these issues, this study applies data mining techniques by combining the K-Means Clustering method and the C4.5 algorithm, aiming to simplify the grouping process and provide a decision tree representation of the analyzed data. This study utilizes data from the years 2023-2024, consisting of 1,357 samples with attributes including the head of the family's name, occupation, number of dependents, income, housing condition, homeownership status, and residents' welfare status. The data was processed using RapidMiner tools and resulted in three clusters: Cluster 1 as the top priority (eligible) with 377 members, Cluster 2 as a consideration with 482 members, and Cluster 3 as the ineligible category with 498 members. The evaluation indicated that the accuracy of the Clustering method, based on the Davies-Bouldin Index (DBI), was 0.187, and silhouette score 0,75 while the accuracy of the C4.5 algorithm reached 99.7%.
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