Comparative Analysis of Incoming Goods Patterns Using FP-Growth and Apriori Algorithms: A Case Study in Retail

Akbar Pramuja Ritonga; Syaiful Zuhri Harahap; Masrizal Masrizal

doi:10.47709/cnahpc.v7i3.6776

Authors

Akbar Pramuja Ritonga UNIVERSITAS LABUHANABATU
Syaiful Zuhri Harahap Universitas Labuhanbatu
Masrizal Universitas Labuhanbatu

DOI:

https://doi.org/10.47709/cnahpc.v7i3.6776

Keywords:

Apriori Method, Confidence, Data Mining, Fp Growth Method, Support,

Abstract

This study aims to analyze consumer purchasing patterns in minimarkets using the Apriori and Fp Growth association algorithms based on transaction data, where the data consists of 10 goods receipt transactions with 7 variable items such as Ultra Milk UHT 250ml, Indomie Goreng Spesial, Beras Ramos 5kg, Teh Cup Sariwangi 25's, Minyak Goreng Bimoli 1L, Soap Bar Lifebuoy 75g, and Mie Lemonilo Goreng 70g. The analysis process is carried out through the preprocessing stage, transformation to binary format, and application of the algorithm with minimum support parameters of 20% and confidence of 50%. The results show that Ultra Milk UHT 250ml has the highest support (0.5) followed by Indomie Goreng Spesial (0.4), while the combination of UHT Milk with Indomie has a support of 0.2; in terms of confidence, a number of rules even reach a perfect value of 1.0, for example the relationship between Teh Cup Sariwangi and Ultra Milk which always appear together. Quantitatively, Apriori produces 25 association rules with a processing time of approximately 2.1 seconds, while Fp Growth produces the same number of rules but is more efficient with a processing time of 1.3 seconds and lower memory usage, so it can be concluded that although both are equal in terms of rule quality, Fp Growth is superior in computational efficiency. This finding has important practical implications for minimarket management, especially to support shelf arrangement strategies, more targeted stock planning, and the preparation of bundling promotions based on product combinations with high confidence, while also showing a scientific contribution in the form of comparing the performance of two association algorithms on incoming goods data that is relatively rarely used in previous studies.

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