Analysis and Visualization of Purchasing Pattern in Retail Product Transaction using Apriori Algorithm
Article Sidebar
Main Article Content
Abstract
The rapid growth of the retail industry generates large volumes of transaction data that can be analyzed to support data-driven business decision making. This study aims to analyze and visualize purchasing patterns in retail product transactions by applying data mining techniques using the Apriori algorithm and business intelligence visualization through Microsoft Power BI. The dataset consists of 1 million retail transactions collected from an open retail transaction repository. The research stages include data collection, transaction data preprocessing, implementation of the Apriori algorithm with a minimum support threshold of 0.002 and a minimum confidence of 0.5, and visualization of the analysis results through interactive dashboards using Power BI and a Python-based application developed with the Streamlit framework. The results indicate that the Apriori algorithm successfully identifies frequent product associations and generates 12 association rules that meet the criteria of strong association rules. Power BI visualizations provide comprehensive insights into transaction trends based on customer categories, store types, payment methods, seasons, and transaction regions. These findings are expected to assist retail companies in formulating marketing strategies, developing product recommendations, and optimizing inventory management in a more effective and data-driven manner. This study contributes by integrating large-scale association rule mining with interactive business intelligence visualization for retail decision support.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
H. Fitriana Dewi, Hanny Hikmayanti Handayani, and Jamaludin Indra, “Implementasi Algoritma Apriori Terhadap Market Basket Analysis Pada Data Penjualan Retail,” J. Inform. Teknol. dan Sains, vol. 4, no. 4, pp. 432–436, 2022, doi: 10.51401/jinteks.v4i4.2182.
M. A. Ekki Pratama, “Application of Association Rule Mining Method Using Apriori Algorithm to Determine the Purchasing Pattern of Home Made Dimsum,” J. Technol. Comput., vol. 1, no. 4, 2024.
M. K. Najib, E. M. Stefany, P. Informatika, and U. T. Madura, “VISUALISASI DATA PENJUALAN SUPERMARKET DENGAN MICROSOFT POWER BI,” vol. 2, no. 12, pp. 921–928, 2024.
I. M. Angeline Ivana, “Implementation of Apriori Algorithm in Identifying Purchase Relationships at Bluder Cokro Pakuwon Mall,” J. Appl. Informatics Comput., vol. 9, no. 2, pp. 556–563, 2025.
A. Pratama Bukhari, R. Hafidz, and R. W. Prio Pamungkas, “Analisis Business Intelligence Data Penjualan Pt Ambulance Pintar 2021,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 4, pp. 7184–7189, 2024, doi: 10.36040/jati.v8i4.10141.
L. A. A. R. P. I Wayan Supriana, “Implementasi Algoritma Apriori sebagai Association Rule Learning untuk Mengidentifikasi Pola Item Dataset Penjualan,” J. Buana Inform., vol. 16, no. 1, 2025.
O. P. M. Dayini Syahirah, Priati, “Association Rule Mining across Multiple Domains: Systematic Literature Review,” Sink. Politek. Ganesha Medan, vol. 9, no. 4, 2025.
E. P. Rohmawan, “Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Decision Tree Dan Artificial Neural Network,” J. Ilm. MATRIK, vol. 20, no. 1, pp. 21–30, 2018.
A. N. R. Handika Attha Maulana, “Apriori-Based Association Rule Mining Approach for Developing a Product Recommendation System in an Agricultural E-Marketplace,” J. SISFOKOM, vol. 14, no. 4, 2025.
M. D. Elsa Rahma Hidayani1*, “Penerapan Algoritma Apriori dalam Pengembangan Sistem Rekomendasi Produk untuk Meningkatkan Penjualan Impulsif melalui Analisis Pola Pembelian,” Merkurius J. Ris. Sist. Inf. dan Tek. Inform., vol. 3, no. 4, pp. 336–349, 2025.
R. R. Avril Firda Amelia, “Association Rule Analysis for Sales Strategy Optimization with Apriori Algorithm Method,” Sist. J. Sist. Inf., vol. 14, no. 4, 2025.
D. Mulyani, E. D. S., SM, N. N. F., Darmawan, A., Wiyono, R. A., Saputra, R. D., & Rohpandi, “Keyword-Based Hadith Grouping Using Fuzzy C-Means Method,” 2020 2nd Int. Conf. Cybern. Intell. Syst., pp. 1–6, 2020.
G. F. Dedy Dwiputra, Agung Mulyo Widodo, Habibullah Akbar, “Evaluating the Performance of Association Rules in Apriori and FP-Growth Algorithms: Market Basket Analysis to Discover Rules of Item Combinations,” J. Word Sci., vol. 2, no. 8, 2023.
S. W. Ermanto, Abdul Halim Anshor, Asep Arwan Sulaeman, “Association Rule to Increase Sales Using the Apriori Algorithm Method,” Brill. Res. Artif. Intell., vol. 4, no. 1, 2024.
D. F. Roja’ Putri Cintani, “Sales Analysis Using Apriori Algorithm,” J. Ris. Inform., vol. 7, no. 4, 2025.
M. R. Moch Syahrir, Rifqi Hammad, Kurniadin Abd. Latif, “Using a Partition System to Improve the Performance of the Apriori Algorithm in Speeding Up Itemset Frequency Search Process,” Sist. J. Sist. Inf., vol. 13, no. 1, 2024.
D. Listriani and A. H. Setyaningrum, “Penerapan Metode Asosiasi Menggunakan Algoritma Apriori Pada Aplikasi Pola Belanja Konsumen (Studi Kasus Toko Buku Gramedia Bintaro),” Int. J. Sci. Eng. Res. (IJ0SER), vol. 3, no. 4, p. 2, 2015.
S. Wu, “An association rule-based approach for frequent item mining of multi-stage access data,” Discov. Comput., vol. 28, no. 139, 2025.
B. O. Elmira Farrokhizadeh, “A Novel Hesitant Fuzzy Association Rule Mining Model,” Lect. Notes Manag. Ind. Eng., pp. 33–41, 2023.
S. N. S. Abdul Halim Hasugian, Muhammad Siddik Hasibuan, “Apriori to Analyze Sales Patterns of Building Tools and Materials,” IT J. Res. Dev., vol. 7, no. 2, 2023.
C. J. Wijaya NG, Robby Sukma, “Optimizing Marketing Strategies Using FP-Growth and Association Rule Mining Algorithms in the Textile Industry,” J. World Sci., vol. 3, no. 5, 2024.
D. E. K. Sonia Marselina, Jajam Haerul Jaman, “Sales Analysis Using Apriori Algorithm in Data Mining Application on Food and Beverage (F&B) Transactions,” J. Appl. Informatics Comput., vol. 7, no. 2, 2023.
N. C. and O.-A. Țicleanu Ioan Daniel Hunyadi, “Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques,” MDPI J., vol. 15, no. 10, 2025.
M. Al, “Machine Learning in Power BI using PyCaret,” Medium, 2020. https://medium.com/data-science/machine-learning-in-power-bi-using-pycaret-34307f09394a.
M. W. A. K. Ekinnisura Kaban, I Gede Mahendra Darmawiguna, “Optimizing Customer Purchase Insights: Apriori Algorithm for Effective Product Bundle Recommendations,” Brill. Res. Artif. Intell., vol. 4, no. 2, 2024, doi: https://doi.org/10.47709/brilliance.v4i2.4981.