<![CDATA[Managing the availability of electronic product stock is a crucial issue in the retail world due to the high variety of products and dynamic consumer purchasing patterns. Inaccuracy in determining the amount of stock can lead to excess inventory or product shortages, which impacts on decreasing operational efficiency. This study aims to apply the FP-Growth algorithm in the data mining process to determine the pattern of electronic product stock availability based on purchase transaction data. The dataset used in this study consists of 150 electronic product purchase transaction data. The main problem faced is the lack of optimal utilization of transaction data to determine the relationship between products that are frequently purchased together. As a solution, this study applies the Frequent Pattern Growth (FP-Growth) algorithm because of its ability to find association patterns without the need to generate candidate itemsets, making it more efficient in data processing. The research process begins with calculating the frequency of item occurrences, determining the minimum support value of 20% (30 transactions), forming an FP-Tree, and mining frequent itemsets and association rules. The results show that Mouse, Laptop, and Keyboard are the items with the highest frequency, respectively 80%, 73%, and 70% of the total transactions. The Mouse–Laptop–Keyboard purchasing pattern has a support value of 55% with a confidence level of 80%. While the Mouse → Keyboard rule yields the highest confidence level of 85%. Based on these results, it can be concluded that the FP-Growth algorithm is effective in identifying purchasing patterns for electronic products and can be used as a basis for decision-making in prioritizing stock availability more precisely and data-driven.]]>
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