Penerapan Metode Data Mining Pada Point of Sale Berbasis Web Menggunakan Algoritma Apriori

Authors

  • Adam Firmansyah Universitas Nasional, Jakarta
  • M Iwan Wahyudin Universitas Nasional, Jakarta
  • Ben Rahman Universitas Nasional, Jakarta

DOI:

https://doi.org/10.30865/mib.v5i3.3085

Keywords:

Data Mining, Algortms Apriori, Association Rules, Support, Confidience

Abstract

To be able to understand which products have been purchased by customers, it is done by describing the habits when customers buy. Use association rules to detect items purchased at the same time. This study uses an a priori algorithm to determine the association rules when buying goods. The results of the study and analyzing the data obtained a statement that using the a priori algorithm to select the combined itemset using a minimum support of 25% and a minimum confidence of 100%, found the association rule, namely, if the customer buys at the same time. Buying goods has the highest value of support and trust. Likewise with the support value of 25%, the confidence value is 100%. In this way, if a customer buys an item, the probability that the customer buys the item is 100%

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Published

2021-07-31

Issue

Vol. 5 No. 3 (2021): Juli 2021

Section

Articles

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