Analysis of Bread Sales Patterns at Queen Bakery Stores Using Algorithms Fpgrowth - Muhammad Ray Pratama Sembiring; Raissa Amanda Putri (Indonesian Journal of Data Science, IoT, Machine Learning and Informatics, 2025)
The culinary industry, particularly the bakery business, is experiencing rapid growth with increasing competition. Changes in consumer trends, the rising number of market players, and fluctuating market dynamics pose significant challenges in maintaining stable sales. Queen Bakery, a bakery located in Medan, faces issues with fluctuating monthly sales, indicating that certain products are less in demand and that more effective marketing strategies are needed. To address this issue, the utilization of technology in data analysis is essential, particularly through the implementation of data mining techniques. Data mining enables the identification of consumer purchasing patterns more accurately and data-driven. One of the most effective algorithms for sales pattern analysis is FPGrowth, which can identify frequently occurring itemsets in transactions. Unlike the Apriori algorithm, which requires extensive computations, FPGrowth is more efficient in discovering product associations frequently purchased together. This study aims to analyze sales patterns at Queen Bakery using the FPGrowth algorithm to provide strategic insights into inventory management and product marketing. The results of this research are expected to assist Queen Bakery in improving operational efficiency, optimizing product offerings, and formulating more competitive business strategies. By implementing data mining, the bakery can gain a deeper understanding of consumer preferences, ultimately enhancing sales performance and competitiveness in the dynamic culinary market.