<![CDATA[Parung District, Bogor Regency, is one of the regions receiving social assistance sourced from the Integrated Social Welfare Data (DTKS). Distribution of social assistance in this area has been supported by available recipient data; however, the data has not been grouped and analyzed systematically based on regional or geographical distribution. Recipient data is still processed in tabular form using spreadsheet files, so related parties find it difficult to quickly identify villages with high, medium, or low concentrations of social assistance recipients. Without spatial visualization, the distribution pattern across regions is difficult to monitor, hindering decision-making related to distribution priorities and oversight. To address this issue, this study applies the K-Means Clustering algorithm to group regions of social assistance recipients based on location coordinates (latitude and longitude). The data used consists of 6,946 rows of Village Fund Direct Cash Assistance (BLT-DD) recipient data spread across 9 villages in Parung District. Data was grouped into three categories (High, Medium, Low), with the number of clusters validated using the Elbow Method and Silhouette Score. The system was implemented using Python with the Flask framework, a MySQL database, and Leaflet.js to display clustering results on an interactive web-based map. Testing shows that K=3 produced a Silhouette Score of 0.5367, verified through manual calculation with 100% agreement with the program output.]]>
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