Identification of Website-Based Product Sales Frequency Patterns using Apriori Algorithms and Eclat Algorithms at Rio Food in Bekasi

Authors

  • Salwa Nabiila Pramuhesti Universitas Bhayangkara Jakarta Raya
  • Herlawati Herlawati Universitas Bhayangkara Jakarta Raya http://orcid.org/0000-0002-4815-9841
  • Tyastuti Sri Lestari Universitas Bhayangkara Jakarta Raya

DOI:

https://doi.org/10.33558/piksel.v11i1.5941

Keywords:

frequency patterns, apriori algorithm, eclat algorithm, association rule mining, product marketing

Abstract

Sales reports that are not managed automatically may hinder businesses from accurately determining their progress in the short or long term. With increasing community needs for a product, business owners have an opportunity to market their products to a larger audience. The abundance of data highlights the need for information to produce patterns that can be used as a reference for making decisions in buying products on the website. Data mining algorithms can provide support for analysis, which can help avoid inaccurate business progress reports. In this study, the Apriori and Eclat algorithms were applied to analyze frequent itemsets in association rule mining. The dataset used in this study consists of 20 transaction data from frozen food sales. The results showed that the combination of Nugget and Chicken Sausage itemsets were the most frequent, with higher support, confidence, and lift ratio values than the others. These results can be used as product recommendations that are most in demand by customers.

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Published

2023-03-29

How to Cite

Pramuhesti, S. N., Herlawati, H., & Lestari, T. S. (2023). Identification of Website-Based Product Sales Frequency Patterns using Apriori Algorithms and Eclat Algorithms at Rio Food in Bekasi. PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic, 11(1), 77–90. https://doi.org/10.33558/piksel.v11i1.5941

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Articles