ENHANCED CLUSTERING USING PSO-KMEDOIDS FOR GOVERNMENT AID DISTRIBUTION - Aulia Nur Fitriani; Kartika Maulida Hindrayani; Trimono Trimono (Jurnal Sistem Informasi dan Informatika (Simika), 2025)
The distribution of social assistance in Indonesia often experiences problems due to inaccuracies in recipient data between those recorded in government systems and field conditions. In Kalipuro Village, Mojokerto District, data mismatches caused difficulties in screening assistance, requiring village officials to manually re-filter the data. This triggered protests from citizens who should have received assistance but did not get their rights. To overcome this problem, this research proposes the use of the K-Medoids algorithm which is able to overcome sensitivity to outliers. This algorithm is used to cluster data based on criteria such as occupation, number of assets, number of dependents, and income. In addition, this research incorporates the Particle Swarm Optimization (PSO) technique to optimise the clustering process, which is expected to improve accuracy and efficiency in social assistance distribution. The results of clustering analysis using the K-Medoids algorithm show that the best cluster is obtained at the number of clusters K=5, with the distribution of cluster 0 (179 households), cluster 1(89 households), cluster 2 (296 households), cluster 3 (354 households), and cluster 4 (94 households). The Silhouette Score value of 0.6531 indicates good cohesion and separation between clusters. Based on the analysis, cluster 1 is the top priority group of aid recipients, followed by clusters 4, 2, 3, and 0. The K-Medoids algorithm effectively identifies the most needy community groups, supporting targeted and efficient decisions in aid distribution.