<![CDATA[The high dropout rate in Asahan Regency has become a serious problem affecting the quality of human resources and equitable access to education across various regions. This study aims to identify patterns and characteristics of dropout-prone areas using the K-Means clustering technique. The research method involves collecting dropout data from the Asahan Regency Education Office for the period 2022–2025, followed by data pre-processing for cleaning and normalization, and then clustering analysis to generate three regional clusters based on dropout vulnerability levels. The results indicate that clusters with high dropout rates are largely influenced by economic factors, followed by limited access to education and social conditions in the community. The resulting regional segmentation provides a spatial overview of dropout vulnerability levels in Asahan Regency. These findings offer data-driven insights that can support the formulation of more targeted education policies and programs to encourage inclusive education development in the region. Scientifically, this study contributes to strengthening the validity and effectiveness of the K-Means algorithm as a quantitative approach in mapping and identifying complex patterns in socio-educational data, thereby expanding its application in data-driven analytical studies in the field of education.]]>
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