<![CDATA[This research aims to help the Ayaa Fashion Store improve its sales strategy by understanding customer shopping patterns. This research was carried out by utilizing the FP-Growth algorithm to analyze consumer purchasing patterns based on sales transaction data at the Ayaa Fashion Store which operates in the sales of women's clothing, mukenas and headscarves. The data collection method is carried out through daily sales transaction information, with a total of 365 transactions recording goods purchased by consumers. The use of the FP-Growth Algorithm allows identifying events that frequently occur in the dataset, producing association rules that describe consumer shopping patterns. The analysis is carried out by paying attention to the support and confidence values, as well as using the lift ratio to assess the strength of the association rules. Data processing was carried out using RapidMiner software. The research results include 12 association rules and 20 itemsets of goods that are often purchased by consumers. The results of this research evaluation show that the FP-Growth algorithm is effective in analyzing sales transaction data, providing valuable guidance for designing sales strategies, and optimizing the supply of goods for Ayaa Fashion Stores.]]>
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