<![CDATA[This study aims to implement the K-Means Clustering algorithm for customer segmentation in e-commerce to enhance marketing strategy effectiveness. By utilizing customer transaction data such as purchase frequency, product quantity, and total spending, the study classifies customers into three main segments: high, medium, and low transaction groups. The research method includes data preprocessing, cluster center initialization, Euclidean distance calculation, and iterative clustering to achieve optimal segmentation. The segmentation results are integrated into a web-based system, facilitating interactive customer data management. Testing on application features, including login, data input, clustering process, and reporting, confirms that the application functions as expected. These findings reinforce the role of K-Means-based segmentation in supporting more targeted marketing decision-making in the e-commerce sector.]]>
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