Application of Data Mining for Ceramic Sales Data Association Using Apriori Algorithm

Authors

  • M. ILHAM HABIBI Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Alwis Nazir
  • Elin Haerani
  • Elvia Budianita

DOI:

https://doi.org/10.30983/knowbase.v5i2.8757

Keywords:

Algoritma Apriori, Support, Confidence

Abstract

This research is conducted to provide an understanding of consumer purchasing patterns at CV. Sukses Bersama by applying data mining using the association rules method and the Apriori algorithm to identify the relationships between one item that influences other items within a ceramic sales dataset at CV. Sukses Bersama. This information is expected to serve as a foundation for improving sales strategies, optimizing customer satisfaction, and expanding the company's market share. The Apriori algorithm is a popular algorithm implemented to identify association rules in data mining. The Apriori algorithm was chosen due to its ability to efficiently identify association rules and its good scalability in handling large datasets. This research begins with the collection of ceramic sales data, followed by data preprocessing to clean and prepare the data. The Apriori algorithm is then applied to discover the association rules, which generate two matrices: support and confidence, and the results are subsequently evaluated. This research was conducted using Google Colaboratory, a web application that is a cloud-based platform provided by Google to run Python code. The results of the study show that the Apriori algorithm can depict significant association structures between different ceramic brand types in the sales data of CV. Sukses Bersama. The calculation results show that the rule has the maximum support and confidence value, namely 67% support value and 84% confidence value in the rule "if you buy the DIAMD brand, you will buy the TOTAL brand"

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Submitted

2024-11-13

Published

2024-12-05

Issue

Vol. 5 No. 2 (2024): December 2024

Section

Articles

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Copyright (c) 2024 M. ILHAM HABIBI, Alwis Nazir, Elin Haerani, Elvia Budianita

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