<![CDATA[This research aims to segment customers at PT. Sanutama Bumi Arto by applying RFM (Recency, Frequency, Monetary) analysis combined with the K-Means Clustering algorithm. RFM analysis is used to identify customer purchasing characteristics based on recency, frequency of purchases and total purchase value (monetary). Then, the K-Means algorithm is used to group customers into different segments based on the similarity of RFM characteristics. This research uses customer transaction data from PT. Sanutama Bumi Arto. The research results show that there are two customer clusters with different characteristics, namely customers with low purchasing levels and customers with high purchasing levels. Customer clusters with high purchasing levels have higher recency, frequency and monetary values compared to customer clusters with low purchasing levels. Cluster evaluation was carried out using the Silhoutte Score (0.44), WSS (972.19) and BSS (1112.73) metrics, which shows that clustering has good performance. It is hoped that the results of this research can provide valuable insight for PT. Sanutama Bumi Arto in understanding customer behavior and developing more effective marketing strategies.]]>
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