Implementasi Data Mining Untuk Analisis Prilaku Konsumen Dalam Meningkatkan Penjualan Di Shopee

  • Asyraf Muntasir Pratama Universitas Dehasen Bengkulu
  • Siswanto Siswanto Universitas Dehasen Bengkulu
  • Ricky Zulfiandry Universitas Dehasen Bengkulu
DOI: https://doi.org/10.37676/jmi.v21i2.9388
Keywords: Data Mining, K-Means, Clustering, Consumer Behavior, Shopee, Anjani_Store.id

Abstract

The rapid growth of e-commerce requires an in-depth understanding of consumer behavior to increase sales. This research applies a data mining method using the K-Means clustering algorithm to analyze weekly revenue data from the Anjani_Store.id store on the Shopee platform, in the period from January to July 2023. The research uses a waterfall approach and is implemented through the Python programming language with the Flask framework. The clustering results produce three groups of consumer behavior based on income levels: low, medium, and high. Clusters are analyzed to support promotional strategies and business decision making. This system was tested and showed that the application of the K-Means method was effective in grouping consumers appropriately and supporting increased sales based on data analysis

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References

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Published
2025-10-08
How to Cite
Pratama, A., Siswanto, S., & Zulfiandry, R. (2025). Implementasi Data Mining Untuk Analisis Prilaku Konsumen Dalam Meningkatkan Penjualan Di Shopee. JURNAL MEDIA INFOTAMA, 21(2), 703-709. https://doi.org/10.37676/jmi.v21i2.9388
Issue
Vol 21 No 2 (2025): Oktober
Section
Articles

Copyright (c) 2025 Asyraf Muntasir Pratama; Siswanto Siswanto, Ricky Zulfiandry

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