<![CDATA[Social assistance distribution requires accurate data management to ensure that assistance reaches eligible beneficiaries. The Binjai City Social Agency manages the National Socio-Economic Single Data (DTSEN), which contains socio-economic information but is primarily used for administrative purposes. This study aims to apply the K-Means clustering algorithm to identify patterns among social assistance recipients based on the National Decile, Family Hope Program (PKH) recipient status, and Basic Food Assistance recipient status. The research employed the Knowledge Discovery in Databases (KDD) methodology, consisting of Data Selection, Data Preprocessing, Transformation, Data Mining, and Evaluation. A total of 847 DTSEN records were analyzed using MATLAB, while the optimal number of clusters was determined using the Elbow Method. The results indicated that the optimal number of clusters was three, representing recipient groups with different socio-economic characteristics based on National Decile and social assistance status. The clustering results provide useful information for understanding recipient characteristics and can support the Binjai City Social Agency in managing social assistance data more effectively.]]>
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