Association Analysis in Java Ateka for Stationery Sales Promotion Using the FP-Growth Algorithm

Wardani, Syafa Wajahtu; Lestari, Silvia Windri; Daffa, Nauffal Ammar; Tahyudin, Imam

doi:10.31763/iota.v2i3.569

Cited by 1 publication

(1 citation statement)

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“…Based on the previous research, the FP-Growth method has proven more reliable in generating association rules from transactional data and exhibits efficiency in rule generation [18,19]. Therefore, this study applies the FP-Growth method to analyze consumer purchasing patterns using supermarket sales data, aiming to effortlessly identify relationships between items frequently purchased together by customers.…”

Section: Introductionmentioning

confidence: 99%

Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data

Hairani,

Ximenes Guterres

2024

IJECSA

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The need to analyze consumer purchasing patterns using association techniques also lies in the increasingly fierce competition in the retail market. Supermarkets face the challenge of understanding their customers' buying patterns. By utilizing association techniques, supermarkets can identify customer buying trends and quickly and appropriately adjust their strategies. Thus, analyzing consumer purchasing patterns using association techniques is no longer an option but an urgent need for supermarkets that want to survive and thrive in a changing market. Therefore, this study aimed to analyze purchasing patterns in supermarkets using the FP-Growth method to understand purchasing behavior and identify relevant patterns from transaction data. The method used in this research was the FP-Growth association method to create association rules from customer transaction data. The findings of this research were the use of the FP-Growth method in analyzing supermarket customer purchasing patterns, which obtained 10 association rules for 2 itemsets and 11 association rules for 3 itemsets based on a minimum Support value of 30% and a minimum Confidence of 70%. The association rules generated by the FP-Growth method on 2 itemsets and 3 itemsets simultaneously bring up items often purchased by customers with the same pattern, namely Cooking Oil, Eggs, Flour, and Candy. This research concludes that the association rules formed can be used as a benchmark by supermarkets in preparing stock items and making strategies to increase sales for more profit.

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Section: Introductionmentioning

confidence: 99%