Implementasi Algoritma Apriori untuk Menemukan Hubungan Antar Produk pada Transaksi Penjualan di Aming Coffe

Bachtiar Aldy Ramadhani; Samidi Samidi

doi:10.30998/jrami.v7i03.1509

Authors

Bachtiar Aldy Ramadhani Author
Samidi Samidi Universitas Budi Luhur Author

DOI:

https://doi.org/10.30998/jrami.v7i03.1509

Keywords:

Penjualan Produk, Market Basket Analysis, Apriori, Analisis Asosiasi, Data Mining

Abstract

Increasingly fierce competition in the coffee shop industry requires business owners to implement effective, data-driven marketing strategies to boost sales of coffee beverages. Market basket analysis Market basket analysis identifies consumer purchasing patterns by analyzing the relationships between frequently purchased products. The goal is to identify consumer purchasing patterns by analyzing the relationships between products that are frequently purchased together. This study aims to optimize the Apriori algorithm for analyzing consumer purchasing patterns in coffee beverage sales at Aming Coffee. This study uses the Cross-Industry Standard Process for Data Mining framework, which includes the stages of business understanding, data understanding, data preparation, modeling, and evaluation. The data used consists of coffee sales transaction data obtained from the iSeller Point of Sale system for the period from January 1, 2024, to May 31, 2025. The analysis process began with the preprocessing of transaction data, followed by the application of the Apriori algorithm to generate frequent item sets and association rules based on the minimum support and minimum confidence thresholds. The results of this study show that the Apriori algorithm, based on the support, confidence, and lift values obtained, meets the evaluation criteria. Optimizing the minimum support and minimum confidence parameters was found to influence the number and quality of the resulting association rules. It is hoped that the results of this study can serve as an analytical reference to support business decision-making, particularly in understanding consumer purchasing behavior regarding coffee beverage sales, as well as provide an academic contribution to the application of data mining techniques based on the Apriori algorithm.

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