Sales Association Analysis at the Donatkoe Factory Store Which is Upgraded using the Fp-Growth Algorithm

Dwina Aurelia Agustina; Nining Rahaningsih; Raditya Danar Dana; Cep Lukman Rohmat

doi:10.59934/jaiea.v4i3.957

Authors

Dwina Aurelia Agustina STMIK IKMI Cirebon
Nining Rahaningsih STMIK IKMI Cirebon
Raditya Danar Dana STMIK IKMI Cirebon
Cep Lukman Rohmat STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i3.957

Keywords:

FP-Growth Algorithm, Purchasing Patterns, Association Rules, Donatkoe Factory Store, Data Mining.

Abstract

In the retail industry, especially food and beverage, understanding customer buying patterns is crucial for effective stock management and marketing strategies. Donatkoe Factory stores faced challenges in identifying items that were frequently purchased at the same time, which often led to operational inefficiencies and lowered profitability. Association analysis is needed to uncover purchasing patterns to support data-driven decision-making. This study uses the FP-Growth algorithm to analyze transaction data at Donatkoe Factory stores. The parameters used are support, confidence, and elevator to evaluate the strength of the relationship between items. Transaction datasets are analyzed to find combinations of products that are frequently purchased together. The results of the analysis showed several product combinations with strong associations, such as Donuts with Pizza (confidence 0.814; elevator 1.031) and Donuts with Fruit Salad (confidence 0.821; elevator 1,039). The combination with the highest confidence was Donuts with Pizza, Fruit Salad, and Buko Pandan (confidence 0.842; elevator 1,066). These findings indicate that the FP-Growth algorithm is effective in identifying relationships between items, so it can support marketing strategies such as adjacent product placements, bundling, or special promotions. The results of this study also provide insights for Donatkoe Factory stores to improve operational efficiency and customer satisfaction through data-driven decisions.

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