<![CDATA[This research aims to apply the Apriori algorithm in analyzing flower purchase patterns at a flower shop. Apriori algorithm is used to identify product combinations that are often purchased together, in the hope of finding purchasing patterns that can be utilized to improve marketing strategies and store operational efficiency. Transaction data from the shop is processed to extract frequent itemsets and generate association rules by setting the right threshold of support and confidence values. The results of this study show that flower combinations such as Tulip and Bougenville frequently co-occur in purchases, with significant support-confidence products. These findings provide insights into consumer purchasing behavior that can be used to recommend product bundling or product rearrangement in stores. This research contributes to the application of data mining in the retail sector, particularly in increasing sales and customer satisfaction in flower shops.]]>
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