Inventory management is a crucial factor in the sustainability of a grocery store, preventing the risk of understocking or overstocking. This research aims to design and implement a web-based system that applies the Apriori algorithm to determine inventory patterns based on previous sales transaction data. The case study was conducted at Kartika Trunojoyo Grocery Store using a data mining approach involving support, confidence, and lift calculations to identify association rules between products. The analysis yielded six association rules with positive correlations. The strongest relationship was found between Cooking Oil ⇒ Cigarettes with a confidence value of 44.23% and a lift of 1.59, and the reverse relationship between Cigarettes ⇒ Cooking Oil with a confidence value of 39.66% and the same lift. The developed system is capable of providing recommendations for items to prioritize in inventory, thus assisting store owners in making procurement decisions. Testing using the Black Box method showed that all system functions functioned as designed. This research proves that the web-based Apriori algorithm can be an effective solution to support efficient inventory management in grocery stores.