Optimasi Strategi Penjualan Am2000 Tirtamart Dengan Algoritma Apriori Untuk Mengidentifikasi Produk Favorit Pelanggan

  • Bambang Sugito * Mail Universitas Pembangunan Panca Budi Medan , Indonesia
  • Sri Wahyuni Universitas Pembangunan Panca Budi Medan , Indonesia
DOI: https://doi.org/10.47065/bit.v5i4.1707
Keywords: Apriori algorithm, data mining, purchasing patterns, sales strategy, Toko AM2000,Optimization

Abstract

The retail industry faces increasing competition, and to survive, companies need to understand customer purchasing behavior and optimize their sales strategies. One effective approach is the use of data mining to analyze sales data and identify purchasing patterns. This study aims to optimize the sales strategy of Toko AM2000 by applying the Apriori algorithm to identify the most popular products among customers. The data used includes sales transactions from January to September 2024, with a total of 1,000 transactions and 10 attributes. The results of the analysis using the Apriori algorithm show a significant association between the products "Water Softener" and "Filter Tank," although the support value obtained, which is 20.4%, does not meet the minimum support threshold of 30%. However, the confidence value of 80.6% indicates a high likelihood that customers who purchase "Water Softener" also buy "Filter Tank." This suggests that Toko AM2000 should focus its marketing strategies on promoting these two products. To improve the effectiveness of the analysis, it is recommended to lower the minimum support value, increase the number of transactions, and consider using other algorithms, such as K-means. This study provides valuable insights for business decision-making and the enhancement of Toko AM2000's marketing strategy.

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Published
2024-12-23
How to Cite
Sugito, B., & Wahyuni, S. (2024). Optimasi Strategi Penjualan Am2000 Tirtamart Dengan Algoritma Apriori Untuk Mengidentifikasi Produk Favorit Pelanggan . Bulletin of Information Technology (BIT), 5(4), 278 - 286. https://doi.org/10.47065/bit.v5i4.1707
Issue
Vol 5 No 4: Desember 2024
Section
Articles

Copyright (c) 2024 BAMBANG SUGITO

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