<![CDATA[The development of information technology in the Industry 4.0 era has driven significant changes in how organizations utilize data to support strategic decision-making. Data utilization is no longer limited to transaction recording but has shifted toward data processing as a source of predictive information that plays an important role in competitive business management. The urgency of this research is reinforced by the relatively low level of adoption of data-driven analytical systems among Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, including the outdoor equipment rental sector. In the modern business environment, decision-making can no longer rely solely on intuition but must be supported by data and predictive analysis to improve efficiency and competitiveness. Therefore, the development of a Smart Inventory Management system based on Business Intelligence, implementing the Apriori and FP-Growth algorithms at SAVANA Outdoor Store, is expected to provide automatic recommendations for inventory requirements based on real and representative historical transaction patterns. Based on the results of processing outdoor equipment rental data using the Apriori algorithm with a confidence value of 68%, several association rules were obtained, indicating a tendency of dissimilar borrowing patterns (mutually exclusive relationships) among certain types of equipment. Meanwhile, the processing results using the FP-Growth algorithm demonstrated better performance. This algorithm successfully generated a total of 21 association rules, with the top ten rules having confidence values ranging from 70% to 72%.]]>
