<![CDATA[This research aims to implement the Apriori algorithm in a web-based fruit sales application to discover consumer purchasing patterns and provide appropriate product recommendations. The dataset used consists of 10 fruit sales transactions. The fruit sales transaction data is then analyzed using the Apriori algorithm to identify frequently purchased product combinations. The analysis results are then used to provide accurate product recommendations to customers. Implementation is carried out by calculating the support, confidence, and lift ratio values for each product combination based on transaction data. Association rules with a support value of 30% and confidence of 60% found sweet fragrant mango, murcot australian orange, and fuji88 apple, which are then used to provide recommendations to relevant customers. This research aims to improve product recommendation accuracy, customer satisfaction, and overall sales efficiency in the fruit sales industry.]]>
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