<![CDATA[Customer segmentation is a crucial process in understanding consumer behavior patterns to support strategic decision making in marketing. The main challenge companies face is accurately segmenting customers based on transaction data. The purpose of this research is to determine and segment customers using the K-Means clustering algorithm based on the RFM (Recency, Frequency, Monetary) model on AMDK sales transaction data at PT XYZ. The research method involves analysis of 111 customer data processed using Orange Data Mining software, with validation of the results using Silhouette Score which is useful in determining the ideal number of clusters. This research produces four customer clusters, with Cluster 4 reflecting customers with the highest level of loyalty, characterized by dominant Frequency and Monetary values, while Cluster 3 describes customers with low loyalty potential. The results of this research provide a scientific basis for the development of more focused and efficient data-based marketing strategies.]]>
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