<![CDATA[Poverty is a social problem that has widespread impacts on society and remains a major concern for governments in their efforts to improve public welfare. This study aims to cluster districts and cities in North Sumatra Province based on the number and percentage of poor people using the K-Means Clustering algorithm. The data were obtained from Statistics Indonesia (BPS) and cover the period from 2019 to 2023. The K-Means algorithm was applied to group regions according to similarities in their poverty characteristics, particularly the number and percentage of poor people. The clustering process was implemented using RapidMiner software to facilitate data processing, analysis, and visualization of the resulting clusters. The findings indicate that K-Means can group districts and cities with similar poverty characteristics, providing an overview of the distribution of poverty across North Sumatra Province. The resulting clusters can serve as supporting information for the regional government in identifying areas that require greater attention and developing more targeted poverty alleviation policies. This study is expected to support more effective resource allocation and contribute to the formulation of sustainable poverty reduction strategies tailored to regional conditions and development priorities.]]>
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