Implementation of Market Basket Analysis on Sales Transactions of a Computer Store Using the ECLAT Algorithm
DOI:
https://doi.org/10.37278/sisinfo.v8i2.1577Keywords:
Market Basket Analysis, ECLAT, Association rules, Sales strategy, Computer storeAbstract
Inventory management and product placement practices at Dunia Computer Store are still conducted conventionally, creating the risk of mismatches between inventory availability and customer demand. To address this challenge, a data-driven approach is required to accurately identify customer purchasing patterns. This study implements the Market Basket Analysis (MBA) method using the Equivalence Class Clustering and Bottom-Up Lattice Traversal (ECLAT) algorithm to discover associations among products that are frequently purchased together. The research process includes transaction data collection, data preprocessing, conversion of transaction data into the Vertical TID-list format, frequent itemset mining, and the generation of association rules based on support, confidence, and lift metrics. In addition, a web-based application was developed to facilitate the implementation of the analysis. The dataset consists of 521 sales transactions from Dunia Computer Store covering the period from January 1 to December 30, 2023. The results indicate that McAfee Anti Virus has the highest support value at 23.22%, followed by Mousepad Deskmat at 14.59% and Logitech Mousepad at 11.52%. The generated association rules reveal significant relationships, such as Logitech Mousepad → McAfee Anti Virus with a confidence value of 75.00% and a lift value of 3.23, as well as RAM Corsair DDR4 8 GB → SSD NVME 512 GB Patriot with a confidence value of 70.37% and a lift value of 6.43. These findings demonstrate that the ECLAT algorithm is capable of efficiently identifying customer purchasing patterns. Therefore, the results can be utilized to support cross-selling strategies, product bundling, product placement optimization, and more effective inventory management based on actual transaction data.
References
R. Haristyarini and W. Yustanti, “Penerapan Metode Market Basket Analysis dengan Algoritma Eclat dan Prediksi dengan Artificial Neural Network pada Data Transaksi Penjualan,” Journal of Emerging Information System and Business Intelligence (JEISBI), vol. 2, no. 3, 2021.
A. I. Septiani, Y. H. Chrisnanto, and F. R. Umbara, “Penentuan Pola Penjualan Produk Toko Elektronik Menggunakan Algoritma Eclat,” in Seminar Nasional Sistem Informasi (SENASIF), vol. 6, 2022, pp. 3059–3070.
E. Darnila, H. Al-Kautsar, and Y. Iksan, “Analysis of Patient Disease Trends Based on Medical Record Data Using the C4.5 Algorithm,” Login: Jurnal Teknologi Komputer, vol. 14, no. 1, pp. 7–12, 2020.
D. Jollyta, W. Ramdhan, and M. Zarlis, Konsep Data Mining dan Penerapan. Yogyakarta: Deepublish, 2020.
L. Zahrotun, “Penerapan Algoritma Eclat untuk Menemukan Pola Asosiasi Antar Barang di Aneka Sandang Collection,” JRST (Jurnal Riset Sains dan Teknologi), pp. 37–43, 2023.
A. Setiawan, V. Kurniawan, and R. Novita, “Penerapan Algoritma Eclat untuk Mencari Pola Hubungan Antar Barang pada Data Transaksi Penjualan,” Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), vol. 4, no. 1, pp. 9–16, 2024.
L. S. Patandean, S. Palelleng, and E. S. Pasinggi, “Market Basket Analysis Menggunakan Algoritma Eclat (Studi Kasus: Transaksi Penjualan di Minimarket),” INFINITY: UKI Toraja Journal of Information Technology, vol. 3, no. 1, pp. 21–25, 2023.
A. P. Bramantyo, R. A. D. Rahajoe, and K. Kartini, “Penerapan Data Mining pada Transaksi Penjualan Selama Bulan Ramadhan untuk Menentukan Market Basket Analysis Menggunakan Algoritma Eclat,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 3, pp. 3879–3886, 2024.
B. A. Nugroho and S. Siswanti, “Pemanfaatan Algoritma Eclat dalam Penemuan Pola Transaksi Penjualan Produk Haircare,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 11, no. 2, pp. 333–341, 2026.
R. F. F. Ahmad, W. Witanti, and E. Ramadhan, “Pola Pembelian Konsumen Supermarket Menggunakan Algoritma ECLAT dan FP-Growth,” Jurnal Algoritma, vol. 22, no. 2, pp. 389–400, 2025.
Y. Naden, S. Sudianto, and U. Athiyah, “Penempatan Produk Penjualan pada E-Commerce Berbasis Perilaku Konsumen Menggunakan Algoritma Eclat,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 4, pp. 7299–7304, 2024.
F. A. Nugroho and B. Pramono, “Komparasi Algoritma Equivalence Class Transformation (ECLAT) dan FP-Growth pada Analisis Data Penjualan,” Jurnal Informatika Ilmu Komputer dan Sistem Informasi, vol. 2, no. 1, 2024.
K. Sussolaikah, “Market Basket Analysis for Determination of Consumer Behavior at XYZ Stores Using R Programming,” Advance Sustainable Science, Engineering and Technology, vol. 3, no. 2, p. 0210206, 2021.
I. Fauziah, “Market Basket Analysis dengan Algoritme Equivalence Class Transformation (ECLAT) pada Pengelolaan Persediaan Menggunakan Economic Order Quantity (EOQ),” 2022.
A. Haikal, Y. H. Chrisnanto, and G. Abdillah, “Product Layout Determination System Using the Association Rules Method Using the Equivalence Class Transformation Algorithm,” Enrichment: Journal of Multidisciplinary Research and Development, vol. 1, no. 6, pp. 299–308, 2023.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Fachri Akbar Nasution, Eva Darnila, Hafizh Al Kautsar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish articles in SisInfo : Jurnal Sistem Informasi dan Informatika agree to the following terms:
- Authors retain copyright of the article and grant the journal right of first publication with the work simultaneously licensed under a CC-BY-SA or The Creative Commons Attribution-ShareAlike License.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
