Analysis of Product Purchase Patterns Using the Apriori Algorithm on FMCG Distributor Transaction Data in the Riau Region
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Abstract
This study investigates purchasing patterns of fast-moving consumer goods (FMCG) in Riau Province, Indonesia, using the Apriori algorithm within the Market Basket Analysis framework. Transaction data from a distributor comprising 4,422 transactions and 243 unique products across Pekanbaru, Kampar, and Rokan Hulu were analyzed to generate frequent itemsets and association rules, evaluated using support, confidence, and lift metrics. The application of a consistent minimum support and confidence threshold ensures statistically robust rule extraction across regions with different transaction scales.The results reveal strong intra-brand associations within the snack category, with several rules exhibiting lift values above ten, indicating systematic bundling behavior rather than random co-occurrence. These findings suggest that retailers tend to stock complementary product variants simultaneously, reflecting structured purchasing patterns at the outlet level. Regional comparison highlights differences in rule density across districts, shaped by transaction volume and the proportional effect of the support threshold, demonstrating how dataset scale influences association complexity. Overall, the study demonstrates that the Apriori algorithm effectively uncovers meaningful purchasing structures in distributor-level transaction data. The findings provide actionable insights for inventory management, regional distribution planning, and targeted promotions, while contributing to the literature by examining FMCG purchasing behavior in a multi-region distribution context using empirical distributor data.
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References
A. B. Prasetio, B. Aboobaider, and A. Asmala, “Interpretable Product Recommendation through Association Rule Mining: An Apriori-Based Analysis on Retail Transaction Data,” Int. J. Informatics Inf. Syst., vol. 8, no. 2, pp. 67–74, 2025, doi: 10.47738/ijiis.v8i2.252.
N. Aini and Z. Fatah, “Implementasi Algoritma Apriori untuk Analisis Pola Pembelian Konsumen pada Dataset Market Basket Analysis,” J. Mhs. Tek. Inform., vol. 4, no. 2, 2025, doi: 10.35473/jamastika.v4i2.4521.
I. F. Rahman and D. Riana, “Market Basket Analysis untuk Penjualan Retail: Perbandingan Akurasi Algoritma Apriori dan FP-Growth Berbasis CRISP-DM,” J. Algoritm., vol. 22, no. 1, 2025, doi: 10.33364/algoritma/v.22-1.2303.
H. F. Dewi, H. H. Handayani, and J. Indra, “Implementasi Algoritma Apriori terhadap Market Basket Analysis pada Data Penjualan Retail,” J. JINTEKS, vol. 4, no. 4, 2022, doi: 10.51401/jinteks.v4i4.2182.
K. Brighton and S. Hariyanto, “Penerapan Metode Market Basket Analisis Dengan Algoritma Apriori Pada Toko Ritel Elektronik,” bit-Tech, vol. 7, no. 1, pp. 37–46, 2024, doi: 10.32877/bt.v7i1.1417.
A. H. Priyanto and Amalia Beladinna Arifa, “Implementation of Market Basket Analysis with Apriori Algorithm in Minimarket,” J. Tek. Inform., vol. 3, no. 5, 2022, doi: 10.20884/1.jutif.2022.3.5.606.
E. Umar, Danny Manongga, and Ade Iriani, “Market Basket Analysis Menggunakan Association Rule dan Algoritma Apriori,” J. Media Inform. Budidarma, vol. 6, no. 3, 2022, [Online]. Available: https://ejurnal.stmik-budidarma.ac.id/index.php/mib/article/view/4217
D. Dwiputra, A. M. Widodo, and H. Akbar, “Evaluating the Performance of Association Rules in Apriori and FP-Growth Algorithms,” J. World Sci., vol. 2, no. 8, 2022, doi: 10.58344/jws.v2i8.403.
H. A. Maulana and A. N. Rohman, “Apriori-Based Association Rule Mining Approach for Developing a Product Recommendation System in an Agricultural E-Marketplace,” J. SISFOKOM, vol. 14, no. 4, 2024, doi: 10.32736/sisfokom.v14i4.2486.
R. Noviana, A. Hermawan, and D. Avianto, “Market Basket Analysis Menggunakan Algoritma Apriori dan FP Growth untuk Menentukan Pola Pembelian Konsumen,” J. Media Inform. Budidarma, vol. 7, no. 3, 2022.
F. Soewignyo, T. I. Soewignyo, W. G. Mokodaser, and A. O. Silitonga, “Evaluasi Kinerja Algoritma Apriori dan FP-Growth untuk Association Rule Mining pada Data Transaksi Ritel,” Techno.Com, vol. 24, no. 4, 2025.
A. Toyin, “Comparative Analysis of Association Rule Mining Algorithms: An Application to Grocery Store,” J. Institutional Res. Big Data Anal. Innov., vol. 1, no. 3, 2025.
S. Putra, D. Selvida, and M. S. Novelan, “Application of Apriori Algorithm in Data Mining to Find Consumer Purchasing Patterns in Supermarkets,” J. Komput. Teknol. Inf. Sist. Inf., vol. 4, no. 1, 2025.
M. L. Prayugo, D. A. Wibowo, M. S. Hidajat, E. Mintorini, and R. R. Ali, “Data Mining Application Analyzing Customer Purchase Patterns Using The Apriori Algorithm,” J. Appl. Intell. Syst., vol. 9, no. 1, 2025.
F. H. Subechi, S. Surorejo, and E. U. Sedya Utami, “Analisis Pola Pembelian Konsumen Menggunakan Algoritma Apriori pada Toko Komputer,” RIGGS J. Artif. Intell. Digit. Bus., vol. 4, no. 3, 2025.
S. A. Miranda, F. Fahrullah, and D. Kurniawan, “Implementasi Association Rule Dalam Menganalisis Data Penjualan Sheshop dengan Menggunakan Algoritma Apriori,” METIK J., vol. 6, no. 1, 2020.
Y. R. A. Siliwangi, A. A. Maftahuhillah, A. N. Rachman, E. N. Fitriani, and G. N. Tarempa, “Implementation of Apriori Algorithm to Analyze Sales Transaction Patterns in Official E-Commerce,” JESII J. Elektron. Sist. Inf., vol. 3, no. 1, 2025.
F. Syaifulloh, E. Y. Puspaningrum, and M. M. Al Haromainy, “Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Squeezer, Apriori dan FP-Growth Pada Toko Bangunan,” Modem J. Inform. dan Sains Teknol., vol. 2, no. 3, 2024.
J. Kang and P. Simanjuntak, “Analisis Pola Penjualan Produk Elektronik pada E-Commerce Menggunakan Algoritma FP-Growth,” Comput. Sci. Ind. Eng., vol. 13, no. 1, 2025.
C. R. Artsitella, A. R. Apriliani, and S. A. Ashari, “Penerapan Association Rules – Market Basket Analysis untuk Mencari Frequent Itemset dengan Algoritma FP-Growth,” J. Al-Azhar Indones. Seri Sains dan Teknol., vol. 6, no. 2, 2020.
N. Wahidi and R. Ismailova, “Association Rule Mining Algorithm Implementation for E-commerce in the Retail Sector,” J. Appl. Res. Technol. & Eng., vol. 5, no. 2, pp. 63–68, 2024, doi: 10.4995/jarte.2024.20753.
B. Mohanty, M. Tripathy, and S. Champati, “Performance Analysis of Association Rule Mining Algorithms: Evidence from the Retailing Industry,” J. Eng. Sci. Technol. Rev., vol. 16, no. 5, pp. 108–122, 2023.
Harlinda and R. Satra, “Data Mining Approach to Improve Minimarket Sales Using Association Rule Method,” J. Inform., vol. 12, no. 1, p. 2025, 2025, [Online]. Available: https://ojs.bsi.ac.id/ejurnal/index.php/ji/article/view/20835
R. Ratnasari, A. F. Yulia, F. Rizki, and A. E. Setiawan, “The Market Basket Analysis In Sales Transactions With Apriori Algorithm,” Int. J. Softw. Eng. Informatics, vol. 1, no. 1, p. 2023, 2023, [Online]. Available: https://journal.aisyahuniversity.ac.id/index.php/IJosei/article/view/pdfratna
S. R. Karimah and E. D. Udayanti, “Comparison of Apriori and FP-Growth Algorithms in Market Basket Analysis for Online Book Sales,” J. Appl. Informatics Comput., vol. 10, no. 1, pp. 566–574, 2026, doi: 10.30871/jaic.v10i1.11921.
Tushar, S. C. Kumar Reddy, and S. Kumar Taliyan, “Market Basket Analysis For Retail Optimization: ‘Identifying Product Associations To Enhance Sales Strategies,’” Int. Res. J. Mod. Eng. Technol. Sci., 2025.
K. Khanna and R. Patira, “Knowledge Discovery in Database and Data Mining,” J. Emerg. Technol. Innov. Res., vol. 10, no. 10, pp. 2310–2623, 2023.
V. Kotu and B. Deshpande, Data Science: Concepts and Practice, 2nd ed. Morgan Kaufmann, 2021.
M. Kaur and S. Kang, “Performance Improvement of Apriori Algorithm Using Pruning Techniques,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 3, 2021.
R. Sharma and A. Singh, “Optimizing Association Rule Mining in Large Transactional Datasets,” Procedia Comput. Sci., vol. 218, pp. 1234–1243, 2023.