<![CDATA[Public sector digital transformation requires a deep understanding of the digital readiness of each administrative region. The Electronic Government System (EGIS) Index is used by the Government of Indonesia as a measuring tool to assess the digital maturity of government agencies. This study aims to cluster districts/cities in West Java Province based on their 2023 EGIS scores to identify hidden patterns of digital readiness. Three unsupervised learning algorithms—K-Means, DBSCAN, and Agglomerative Clustering—are used to explore data-driven regional segmentation. The analyzed dataset includes 27 administrative regions and a number of numerical features related to the EGIS dimensions. The results show that each method is able to form clusters that reflect variations in digital readiness, with DBSCAN producing the most detailed segmentation and being able to detect outliers. Agglomerative Clustering shows good hierarchical separation, while K-Means provides a fairly representative general division. This study provides an analytical basis for contextual and targeted cluster-based policy making in developing regional digital transformation.]]>
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