<![CDATA[The rapid growth of digital technology and internet penetration has contributed to the expansion of e-commerce activity across cities and regencies in Indonesia. However, the intensity of online shopping does not necessarily correspond directly to regional income per capita. This study aims to examine the relationship between online shopping activity and income per capita and to classify Indonesian cities and regencies based on these characteristics using the K-Means Clustering algorithm. This research employs a quantitative approach with a descriptive-exploratory design. Secondary data were collected from statistical and e-commerce sources and processed through data cleaning, transformation, and normalization. The K-Means Clustering algorithm was subsequently applied to group regions according to their levels of online shopping activity and income per capita. The Elbow Method was used to determine the optimal number of clusters. The results identified three clusters with distinct levels of e-commerce activity. Cluster 0 represented regions with the highest level of activity, followed by Cluster 1 and Cluster 2. Several regions demonstrated relatively high levels of e-commerce activity, including Papua, Central Java, West Java, Riau, and DKI Jakarta. These findings provide a regional overview of e-commerce activity and income characteristics that can support digital business strategies and contribute to the development of more inclusive and equitable digital economic policies.]]>
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