<![CDATA[Inventory management and sales analysis in convenience stores are often handled manually, making it difficult for store owners to understand customer purchasing patterns and make effective operational decisions. This study analyzes transaction and inventory data from a convenience store in Kediri by combining Market Basket Analysis and machine learning to produce more measurable, actionable insights. The methods include FP-Growth to discover product associations, Decision Tree to predict inventory status (restock/overstock), K-Means to segment products, and Random Forest Regression to forecast monthly profit. The results show that consistent purchasing patterns can support bundling and product arrangement recommendations, while the classification, clustering, and regression models help improve stock monitoring, wholesale strategy, and financial planning. All outputs are implemented in an interactive dashboard to support practical use by the store owner.]]>
Copyrights © 2026
