Implementation Frequent Pattern Growth (FP-Growth) Algorithm on Market Basket Analysis for Determining Consumer Purchasing Patterns (Case Study: XYZ Coffee Shop)

Globally, the food and beverage industry continues to experience rapid expansion in multiple regions. In following the COVID-19 pandemic, the food and beverage industry has experienced a gradual but consistent expansion. In Indonesia, the food and beverage industry is a thriving economic sector, driven by the country's expanding population and the commensurate rise in demand for food and beverages. Coffee shops are one of the segments of the food and beverage industry. In determining consumer interest in buying coffee drinks, promotional strategies are used to attract customers who are initially not interested in making purchases, thereby increasing the customer's desire to get and or be interested in the product. FP-Growth is a data mining technique that identifies frequently occurring items without requiring candidate production. The results obtained products with a minimum support value of 50%; if a customer buys Pentol 8K, then the customer will also buy KS Hazelnut with a support value of 58.5%. In the interim, the confidence value is 85.7%. The rules are attained with a minimum support value of 50% and a minimum confidence of 80% if KS Hazelnut and Pentol 8K are purchased. Thus, when consumers buy KS Hazelnut, they will buy Pentol 8K food, used as a sales marketing parameter and promotional material.