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Anggi Muhammad Rifa'i
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Other

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Journal : Bulletin of Computer Science Research

 1 
Implementasi Algoritma Apriori dalam Menemukan Pola Asosiasi pada Data Penjualan Produk Retail Butsianto, Sufajar; Candra Naya; Anggi Muhammad Rifa'i
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.731

Abstract

This study aims to implement the Apriori algorithm in finding association patterns in retail product sales data, using the Association Rule Mining approach. Evaluating the ruler or association rules formed based on the support, confidence, and lift parameters, in finding association patterns in retail product sales data with a focus on the relationship between product categories. The data used consists of 500 sales data as sample data and 5,972 transactions as test data. The data mining process was carried out on the main product categories such as Milk, Coffee, Tea, Drinks, Detergent, and Biscuit/Snacks, to find association rules that appear simultaneously with the Bulk Products category in one transaction time. The minimum support parameter was set at 0.02 and the minimum confidence was set at 0.5. By using these parameters, several significant association rules were obtained. One of the strongest rules shows that if products in the Milk, Coffee, Tea, Drinks, Detergent, and Biscuit/Snacks categories are purchased together, then there is a 64.3% probability (confidence) that products in the Bulk Products category are also purchased at the same time. The support value of this rule reached 3.8%, and the lift value was 1.49, indicating a positive association and not a coincidence. Evaluation of the test data showed that this pattern was consistently found across 5,972 transactions, with a repeatability rate of 61.7%. The results of this study demonstrate that the Apriori algorithm is effective in identifying consumer purchasing patterns that can be utilized for product placement strategies, bundling offers, and inventory planning in retail management.
Co-Authors Abdul Ghofir Agung Firmansyah Ahmad Turmudi Zy Arwan, Asep Butsianto, Sufajar Candra Naya Darmasetiawan, Martin Dodit Ardiatma Effendi, Muhammad Makmun Elfandry Bayunanda Fahma Inti Ilmawati Isarianto Kusrini Kusrini M. Ilham Arief Mohammad Rezza Pahlevi Muhamad Ryandy Ghonim Asgar Muhammad Fatchan Muhammad Salimy Ahsan Muhammad Salimy Ahsan Nanang Tedi Kurniadi Nugroho, Agung Pradini, Purnama Sakhrial Rajnaparamitha Kusumastuti Risna Sari Sari, Risna Viyan Qomarudin Noor