Application Of Market Basket Analysis For Sales Transaction Analysis Using Association Fp-Growth Algorithm

Abstract

In an increasingly competitive business world, leveraging transaction data has become crucial for understanding consumer behavior and designing effective marketing strategies. This study aims to apply the FP-Growth algorithm in Market Basket Analysis (MBA) to identify consumer purchase patterns at KS Swalayan. The data analyzed in this research was taken from sales transactions that occurred during October 2024, with key attributes including product codes, product names, quantity, unit price, total price, and discounts. This research follows the Knowledge Discovery in Databases (KDD) framework, which includes stages of data selection, data cleaning, transformation, pattern collection, and result evaluation. The research findings indicate that the FP-Growth algorithm successfully identified significant associative relationships between various products. For example, there is a relationship between the products "Snack and Roti" and "Susu," which shows a lift value of 1.414861701, indicating a strong correlation between them. These findings provide the basis for marketing strategy recommendations such as product bundling, optimizing shelf layouts, and more efficient stock management. Additionally, the results of this study have the potential to improve consumer shopping experiences by offering products that are frequently bought together. Overall, this study highlights the effectiveness of the FP-Growth algorithm in uncovering consumer purchase patterns, which can support data-driven decision-making and improve marketing strategy efficiency in the retail sector. The implementation of this technique can serve as a valuable tool for store managers to enhance their competitiveness and business performance.