Apriori to Analyze Sales Patterns of Building Tools and Materials

Abdul  Halim Hasugian; Muhammad  Siddik Hasibuan; Siti Nurhaliza Sofyan

doi:10.25299/itjrd.2023.10544

Authors

Abdul Halim Hasugian Faculty of Science and Technology, Universitas Islam Negeri Sumatera Utara
Muhammad Siddik Hasibuan Faculty of Science and Technology, Universitas Islam Negeri Sumatera Utara
Siti Nurhaliza Sofyan Faculty of Science and Technology, Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.25299/itjrd.2023.10544

Keywords:

Pattern of Sales Building Tools and Materials, Data Mining, Apriori Algorithm

Abstract

The accumulation of transaction data on the sale of building tools and materials, which is increasing day by day, should be used as information that can support sales in stores. Consumer purchasing patterns are the main source of data processing. In this study, it is necessary to have a system to process the transaction data based on trends that appear simultaneously in one transaction. The data used is 1000 sales data from December 2020 to May 2021 by calculating the minimum value of support and minimum confidence as a benchmark in the apriori algorithm process, results obtained are in the form of output which is an association rule that will be used by the store. Input the data in the application after that obtain results item set combination. After that these are obtained by calculating the minimum support. The results obtained are processed again with minimum confidence then the results are being final results of association rules that can be used by stores. If the minimum support is 0.1 (10%) and the minimum confidence is 0.5 (50%), then 122 association rules are obtained, one of which is in the 100th order, "If a customer buys a VSB Board, the customer also buys Jumbo Furing with a support value of 23, 3% and a confidence value of 82.9%”. Association rules as the final result are to evaluate, carry out sales strategies, alternative decisions in stocking goods and determine the placement of goods close together.

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