Syabri, Muhammad Rodhian2023-01-262023-01-262022-10PerpustakaanElfitrahttps://repository.unri.ac.id/handle/123456789/10829Product segmentation is one of the marketing strategies used by business units to achieve goals, which include decisions regarding target markets, product placement, and product promotion. To could perform product segmentation more effectively and efficiently, it is necessary to process sales data, by grouping product data from the level of sales. Er Coffee as the object of research, has a problem with how to group products based on the level of sales, to determine the right strategy. The sales transaction data used in this research are 27330-row records consisting of 186 product data with attributes used are the number of items and unit prices. This study uses the K-Medoids using the Minkowski Distance to group product data into 3 clusters based on high, medium, and low sales levels with the aim that each group will produce different promotional strategies for each level of sales. The product grouping analysis system in this research is web-based using the PHP programming language. The cluster results from this study were 76 items with low sales levels, 106 medium sales products, and 4 high sales products, then an evaluation of the cluster results was carried out to determine how well the accuracy level of the cluster results was. Testing the results of clustering using Silhouette Coefficient, based on this process, the k-medoids are good at grouping products with an accuracy rate of 0.570 and are included in the medium clusteringenProduct segmentationClusteringProductK-MedoidsSilhouette CoefficientArticle