<![CDATA[Pangkalan Sudiawati Bekasi is a business engaged in household goods distribution with a central warehouse that stores various products. However, the sales data management process is still conducted manually through bookkeeping, which often leads to recording errors and difficulties in identifying the most demanded products. This issue results in ineffective decision-making regarding stock procurement, potentially causing losses due to unsold products. This study aims to design a sales data management system that can effectively identify best-selling products. The approach used in this research is the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology as a system development framework, combined with the K-Means Clustering algorithm to group sales data based on product demand levels. The results of this study indicate that the developed system is capable of classifying products into several categories, such as highly demanded, moderately demanded, and less demanded products. This classification assists Pangkalan Sudiawati in making more accurate decisions regarding inventory management and improving the efficiency of sales data processing.]]>
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