<![CDATA[The assessment of household economic welfare is still often carried out manually, making the process time-consuming and potentially less objective. This condition makes it more difficult for the government to accurately identify beneficiaries of social assistance programs. This study aims to classify the economic welfare levels of households in Sukamakmur Village using the K-Means Clustering method as a decision support tool. The research data were collected through questionnaires distributed to 157 households using four variables: income, number of dependents, education level, and home ownership. Before the clustering process, the data were normalized to ensure that all variables were measured on the same scale. The analysis included data normalization, distance calculation using Euclidean Distance, K-Means clustering with three clusters (K=3), and validation of the results using RapidMiner, Weka, and Orange. The results showed that the 157 household records were successfully grouped into three clusters: Cluster 1 with 46 households, Cluster 2 with 75 households, and Cluster 3 with 36 households. The validation results indicated that the three software tools produced relatively consistent clustering patterns, with Weka providing results that were closest to the manual calculations. This study is expected to assist the Sukamakmur Village government in formulating policies and distributing social welfare programs more accurately.]]>
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