<![CDATA[The advancement of digital technology has improved data management, including in the distribution of social assistance. However, the large volume of data and the similarity of community characteristics often hinder the manual determination of aid recipients. This study analyzes the performance of two clustering algorithms, K-Means and K-Medoids, in grouping social assistance recipients in Kelurahan Terjun. Using a quantitative approach and data mining techniques based on clustering. The data is divided into three groups: Eligible, Not Eligible, and Requires Validation. The results show that although both algorithms produce similar clustering patterns, K-Medoids demonstrates better performance in cluster distribution and visualization. Cluster visualization using PCA indicates that K-Medoids forms clearer cluster boundaries and more balanced data distribution compared to K-Means. It can be concluded that K-Medoids outperforms in clustering social assistance recipient data and can serve as a more efficient alternative for targeted aid distribution.]]>
Copyrights © 2025
