<![CDATA[Interregional socio-economic disparities in Indonesia present both obstacles and opportunities for the expansion of digital business. This research aims to perform regional segmentation based on the Human Development Index (HDI), unemployment rate, and poverty percentage in order to formulate targeted recommendations for digital business strategies. The analytical method employed is K-Means Clustering, applied to data from 41 regencies and municipalities across Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). The dataset was sourced from Statistics Indonesia (BPS) for the year 2025. The findings reveal four distinct clusters with unique characteristics. Cluster 1 (advanced regions) features an HDI exceeding 83 and a poverty rate below 4%, making it suitable for premium digital services and on-demand business models. Cluster 2 (developing regions) has an HDI between 75 and 80 and poverty under 5%, indicating strong potential for e-commerce and fintech platforms. Cluster 3 (transitional regions) demonstrates an HDI of 70–75 with a poverty rate of approximately 12%, aligning well with digital education and healthcare services. Cluster 4 (lagging regions) reports an HDI below 70 and poverty exceeding 22%, necessitating an inclusive strategy focused on digital services that address basic needs. In conclusion, socio-economically driven regional segmentation proves effective in designing digital business strategies that are responsive to the distinct profiles of each cluster. Keywords : Regional Segmentation, Digital Business, K-means clustering, Human Development Index, Digital Divide]]>
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