Grouping of Social Assistance Recipients Using K-Means Algorithm (Case Study: Gegunung Village Office)

Abstract

The aim of this research is to use the K-Means algorithm to classify social assistance recipients in Gegunung Village based on location and nominal data. Inaccuracy and inefficiency are the main problems in the distribution of social assistance, so a technique is needed that can target grouping of recipient data. The Knowledge Discovery in Database (KDD) stage was used to process 672 data entries, including nominal information, location, occupation and type of assistance. Clusters were created using the K-Means method based on location and nominal value, and the Davies Bouldin Index (DBI) was used to assess quality. The findings show that six clusters with different data distributions were produced by optimal clustering with K=6 and DBI 0.971. Relevant parties can identify more effective distribution strategies with the help of these clusters, which provide insight into more structured social assistance distribution patterns. In short, the K-Means algorithm can be a useful tool for classifying social assistance data, facilitating more informed and effective decision making. This study significantly advances the domain of social assistance management and data collection.