<![CDATA[Mahkota Mart is experiencing stock management problems. This minimarket often encounters mismatches between available stock and sales levels, leading to either shortages or excess inventory. To address this issue, data mining techniques using the K-Means Clustering method are needed to group sales data based on similar product characteristics, thereby facilitating more efficient stock management. The results of this research, processed using tools such as RapidMiner, show that the items in Mahkota Mart can be grouped into three clusters: Cluster 0 includes 61 items (21.40%), categorized as highly in-demand items; Cluster 1 includes 218 items (76.49%), categorized as moderately in-demand items; and Cluster 2 includes 6 items (2.11%), categorized as non-selling items. It is recommended to always replenish the stock of items in Cluster 0. For items in Cluster 1, stock should be added, but in smaller quantities than for Cluster 0. Items in Cluster 2 are recommended not to be restocked to avoid accumulation.]]>
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