<![CDATA[This study was motivated by the manual transaction recording system used at Lapak Pak Iyan, an MSME engaged in scrap goods collection in Rawalumbu, East Bekasi. This condition makes it difficult for the owner to identify frequently traded types of scrap goods, so purchasing decisions are not yet supported by structured data. The research aims to apply data mining using the Naive Bayes algorithm to classify scrap goods based on sales frequency into three categories: High-Selling, Moderate-Selling, and Low-Selling. The research methods include data collection, preprocessing, calculating item frequencies, category labeling, and classification using RapidMiner Studio. The dataset consists of 63 transaction records from January to June 2026 covering seven types of scrap goods. Model testing is conducted using Leave-One-Out Cross Validation (LOOCV) because the available dataset is relatively small. The classification results are expected to provide practical guidance for the owner in prioritizing purchasing decisions based on historical sales patterns. Naive Bayes successfully classified the seven types of scrap goods into three categories with an accuracy of 100%. Therefore, the results can be used as a reference to improve stock management efficiency.]]>
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