Published: 2023-12-20
Analysis of the Apriori Algorithm for Enhancing Retail Product Staple Sales Recommendations
DOI: 10.35870/ijsecs.v3i3.1877
Avip Kurniawan, Niko Suwaryo
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Avip Kurniawan:
Universitas Krisnadwipayana
- Niko Suwaryo: Universitas Medika Suherman
Abstract
Products are fundamental commodities in the market that cater to various consumer needs and desires. This research employs the Apriori algorithm to generate product recommendations based on the analysis of high-demand patterns arising from product sales and association patterns. Specifically, we focus on identifying elevated sales in categories such as Bulk Products, Biscuits/Snacks, Drinks, Milk/Coffee/Tea, and Sauces & Spices during specific time intervals. The model's evaluation and validation entail measuring the Lift Ratio value, a key metric. In our assessment using the RapidMiner Studio application, we find that the Lift Ratio value equals 1. Consequently, our model asserts that combinations with a Lift Ratio value greater than or equal to 1 are deemed valid and beneficial.
Keywords
Retail Products; Data Mining; Apriori Algorithm; Product Recommendations; Fundamental Ingredients
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 3 No. 3 (2023)
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Section: Articles
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Published: 2023-12-20
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License: CC BY 4.0
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Copyright: © 2023 Authors
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DOI: 10.35870/ijsecs.v3i3.1877
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Avip Kurniawan, Universitas Krisnadwipayana
Informatics Engineering Study Program, Faculty of Engineering, Universitas Krisnadwipayana, Bekasi Regency, West Java Province, Indonesia
Niko Suwaryo, Universitas Medika Suherman
Digital Business Study Program, Faculty of Social Sciences and Technology, Universitas Medika Suherman, Bekasi Regency, West Java Province, Indonesia
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