Abstract
The sales of pajama products on i_docraft have not yet leveraged data mining algorithms to analyze transactional data for optimizing sales. To avoid underperforming pajama models and determine which pajama models sell well, the utilization of the Apriori algorithm is necessary. The Apriori algorithm can discern these patterns based on transactional data. This study conducts a transactional data analysis using data mining with the Apriori algorithm. By employing this algorithm, the most frequently sold pajama products can be identified, allowing for prioritization of these models and the development of marketing strategies for other types of pajamas based on a comparison of their strengths and commonly high sales figures. The processed data yields associations rules for concurrently sold pajama items. Based on the results of the final association rules meeting both predetermined minimum support and confidence criteria, for instance, if a product with item code 7 (Cherrypie Nightdress) is purchased, then a product with item code 17 (3 in 1 Lotso Set) will likely be bought with a support value of 22.58% and a confidence value of 100%.
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