<![CDATA[This study aims to develop an application that applies the K-means clustering algorithm to menu data from Tetra CoffeeShop, grouping menu items based on specific characteristics including name, price, transactions, and stock. The process involves uploading an Excel file containing the data, data preprocessing, and clustering. The analysis results indicate effective grouping of menu items using the K-means algorithm, with the optimal number of clusters determined using Elbow method and Silhouette Score. Visualizations such as scatter plots, histograms of price, sales, and stock distribution provide insights into patterns within the data. These findings help understand customer preferences and stock management, potentially enhancing operational efficiency at Tetra CoffeeShop. This study underscores the importance of data analysis in business decision-making.]]>
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