(Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara, Indonesia)
(2) Sudi Suryadi
(Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara, Indonesia)(3) Syaiful Zuhri Harahap
(Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara, Indonesia)(4) Angga Putra Juledi
(Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara, Indonesia)*Corresponding author
AbstractAdvances in information technology have encouraged the use of sales data as a strategic source of information for micro-enterprises. Kopi Keliling Sekojab, a micro-enterprise, generates sales transaction data that has the potential to be analyzed to identify consumer purchasing patterns. However, this data is generally used for administrative purposes without in-depth analysis. This study aims to analyze sales patterns at Kopi Keliling Sekojab by implementing the Apriori and FP-Growth algorithms. The research method used is data mining with a quantitative approach, through the Knowledge Discovery in Database (KDD) stages, which include data collection, pre-processing, data transformation, algorithm application, and analysis of the results. The analyzed data consisted of 30 sales transactions, which were processed to determine support and confidence values to form association rules. The results show that the Apriori and FP-Growth algorithms are capable of identifying customer purchasing patterns, with FP-Growth generating more and more efficient association rules than Apriori. The obtained patterns can be utilized by Kopi Keliling Sekojab businesses in developing sales strategies, stock management, and data-driven service improvements.
KeywordsData Mining; Apriori; FP-Growth; Sales Pattern; Mobile Coffee
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DOIhttps://doi.org/10.33122/ejeset.v7i1.1394 |
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