<![CDATA[The Ayu Collection store sells various types of clothing and accessories. One way to maintain the customers’ satisfaction is by keeping the stock of goods so that no items are empty. The seller must analyze which item data are selling well and which item data is not selling well, based on the sales report data. This problem can be solved by using one of the techniques in data mining, namely the K-Means Clustering algorithm. This research was intended to help the Ayu Collection, a shop in Blora City that sells clothing and accessories, classify its sales data to maximize its stock management. The variables used were the name of the goods, the data of incoming goods, the data of outgoing goods, and the stock of goods. The shop owner can see the results of grouping clothes and accessories that are best-selling and not selling well. Therefore, if there are products that are not selling well, the shop owner can look for other alternatives so that clothes and accessories that are not selling well can be sold. The methods used in the data collection were observation and interviews with the shop owner of the Ayu Collection store. The accuracy of this system reached 83.33% DOI : https://doi.org/10.33005/sibc.v15i1.2754]]>
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