<![CDATA[The growth of coffee menu variations requires business owners to understand consumer interest characteristics in a structured manner, while menu data and consumer preference information are often not fully utilized in decision making. This study aims to segment coffee menus based on consumer-interest characteristics using a data-mining approach. The method applied is clustering using the K-Means algorithm implemented in the Orange Data Mining software, with two main attributes: price and interest category. The analysis process includes data preprocessing, determining the optimal number of clusters, and evaluating cluster quality using the silhouette coefficient. The results show that the K-Means algorithm successfully groups coffee menus into three clusters with distinct price ranges and consumer-interest characteristics. The evaluation yields a silhouette coefficient of 0.725, indicating a strong cluster structure with clear separation between groups. Visualization of the clustering results reveals three main segments: an economical cluster characterized by low prices and high consumer interest, a middle cluster with moderate prices and varying levels of interest, and a premium cluster with high prices and consistently strong consumer interest. These segmentation results provide a clear representation of consumer preference patterns and support decision making in product planning and pricing strategies for coffee businesses.]]>
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