Analysis of Beverage Sales Data Using the FP-Growth  Algorithm at Sini Aja Cafe

Widisa Adi Kumara; Rini Astuti; Willy Prihartono; Tati Suprapti

doi:10.59934/jaiea.v4i2.772

Authors

Widisa Adi Kumara STMIK IKMI Cirebon
Rini Astuti STMIK LIKMI Bandung
Willy Prihartono STMIK IKMI Cirebon
Tati Suprapti STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.772

Keywords:

Sales Data, Association Rules, Frequent Itemset, FP-Growth, Knowledge Discovery in Databases (KDD)

Abstract

The growth of information technology and data mining techniques has greatly helped analyze consumer purchasing behavior, particularly in marketing and inventory management. This study aims to uncover association patterns between products frequently bought by customers at Sini Aja Cafe and to measure these patterns' support and confidence values. The research uses Knowledge Discovery in Databases (KDD), including stages like data selection, preprocessing, transformation, applying the FP-Growth algorithm, and interpreting results. Data from 1,083 beverage sales transactions at Sini Aja Cafe from August 1 to October 31, 2024. The findings reveal five significant association rules when applying a minimum support of 0.1 (10%) and confidence of 0.3 (30%). Notably, if customers buy Red Velvet Oreo, there is a 56% chance they will also buy Thai Tea. Thai Tea sales dominate with a support value 0.557 (55.7%). The support values of the association rules range from 0.141, categorized as medium, and the confidence values range from 0.235, categorized as low. These findings offer valuable insights for the cafe owner to optimize operations, enhance customer satisfaction, and increase profits.

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