<![CDATA[Purpose: Sales of car spare parts at the Multi Topindo Tanjungpinang Workshop play a vital role in maintaining customer satisfaction in after-sales services. Accurate data analysis is required to increase marketing effectiveness, inventory management, and customer satisfaction. One relevant approach is the application of the K-Means Clustering algorithm, which effectively groups car spare part stock data based on the initial stock and final stock. Using this method, workshops can segment stock inventory based on customer preferences by observing what spare parts are sold frequently by comparing them with the initial stock. This segmentation provides the basis for developing a more efficient sales process and precise stock management. Methodology/approach: The research was carried out at the Multi Topindo Tanjungpinang Workshop with system development developed based on the Rapid Application Development (RAD) methodology, which consists of four stages: identifying goals and information needs, working with users to design the system, building the system, introducing a new system, and carrying out classification. spare parts sales stock data by using the k-means method. Results/findings: The results of segmentation obtained through K-Means Clustering will help in identifying spare part groups based on the clusters that are built. In addition, this algorithm plays a role in managing inventory stock, minimizing the risk of excess or shortage of inventory, and increasing overall operational efficiency. Limitations: The system was developed based on the K-Means Algorithm with Python programming language and Django web framework as a sample of data using a car spare parts sales inventory. Contribution: This research contributes to the Multi-Topindo Tanjungpinang workshop, which can take more appropriate steps to meet customer demand, optimize inventory, and ultimately increase customer satisfaction.]]>
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