Implementation of the Apriori Method in Consumer Purchasing Patterns at PT. Ouzen Anugerah Indonesia
DOI:
https://doi.org/10.35335/jict.v16i2.264Keywords:
Apriori Algorithm, Consumer Behavior, Data Mining, Purchasing Pattern Analysis, Retail StrategyAbstract
The rapid growth of the cosmetics industry has created intense competition among retail companies, including PT. Ouzen Anugerah Indonesia, which faces challenges in understanding consumer purchasing behavior due to the lack of systematic data analysis. This study aims to analyze consumer purchasing patterns and identify frequently purchased product combinations using the Apriori algorithm as a data mining technique. The research employed a descriptive quantitative approach based on 5,000 sales transactions recorded between January and December 2024, consisting of transaction IDs, dates, and purchased product lists. Through the Apriori algorithm, the study generated association rules that reveal relationships among products, represented by support and confidence values. The results show that several cosmetic items exhibit strong associative relationships, indicating consistent consumer purchasing tendencies that can be utilized for cross-selling strategies, promotional bundling, and inventory optimization. These findings highlight the effectiveness of data mining in transforming raw transaction data into actionable insights that support data-driven business decision-making. The study contributes theoretically by reinforcing the application of association rule learning in medium-scale retail contexts and practically by providing a framework for developing marketing and operational strategies based on data analysis. The research also suggests that future studies integrate broader datasets and additional variables, such as consumer demographics and temporal factors, to enhance analytical depth and model generalization.
References
Abidin, Z., Amartya, A. K., & Nurdin, A. (2022). Penerapan algoritma Apriori pada penjualan suku cadang kendaraan roda dua (Studi kasus: Toko Prima Motor Sidomulyo). Jurnal Teknoinfo, 16(2), 225. https://doi.org/10.33365/jti.v16i2.1459
Adawiyah, Q., Defit, S., & Sumijan. (2024). Penerapan algoritma K-Means clustering untuk mengelompokkan rekomendasi metode kontrasepsi berbasis machine learning di Puskesmas. Jurnal KomtekInfo, 300–305. https://doi.org/10.33365/j.komtekinfo.vxxi.xx
Ade Irma Amanda, S. M. A., Setiawan, D., & Trisnawati, L. (2023). Penerapan algoritma Apriori dalam menganalisis pola minat beli konsumen di coffee shop. JEKIN - Jurnal Teknik Informatika, 3(1), 25–32. https://doi.org/10.58794/jekin.v3i1.483
Afisyah, A., Winata, H., & Syahputra, Y. H. (2021). Implementasi data mining dengan metode FP-Growth untuk strategi promosi pada Toko Cool Kids Plaza Medan Fair. Jurnal Cyber Tech. http://ojs.trigunadharma.ac.id/index.php/j
Edy Prayitno, & Fakta Sari, D. (2022). Implementasi algoritma Apriori untuk pola kombinasi pembelian barang. Jurnal Cakrawala Ilmiah, 2(2), 691–696. https://doi.org/10.53625/jcijurnalcakrawalailmiah.v2i2.3812
Han, J., Kamber, M., & Pei, J. (2022). Data mining: Concepts and techniques (4th ed.). Morgan Kaufmann.
Hermiati, R., Asnawati, A., & Kanedi, I. (2021). Pembuatan e-commerce pada Raja Komputer menggunakan bahasa pemrograman PHP dan database MySQL. Jurnal Media Infotama, 17(1), 54–66. https://doi.org/10.37676/jmi.v17i1.1317
Janis, J. W., Mamahit, D. J., Sugiarso, B. A., Rumagit, A. M., Elektro, T., Sam, U., & Manado, R. (2020). Rancang bangun aplikasi online sistem pemesanan jasa tukang bangunan berbasis lokasi. Jurnal Teknik Informatika, 15(1), 1–12. https://doi.org/10.35793/jti.15.1.2020.29023
Kotu, V., & Deshpande, B. (2020). Predictive analytics and data mining: Concepts and practice with RapidMiner (2nd ed.). Morgan Kaufmann.
Larose, D. T., & Larose, C. D. (2016). Discovering knowledge in data: An introduction to data mining (2nd ed.). Wiley.
Naldy, E. T., & Andri, A. (2021). Penerapan data mining untuk analisis daftar pembelian konsumen dengan menggunakan algoritma Apriori pada transaksi penjualan Toko Bangunan MDN. Jurnal Nasional Ilmu Komputer, 2(2), 89–101. https://doi.org/10.47747/jurnalnik.v2i2.525
Nisa, V. C., & Khasanah, F. N. (2023). Algoritma Apriori dalam identifikasi pola pembelian konsumen pada produk minuman. INFORMAL: Informatics Journal, 8(2), 156. https://doi.org/10.19184/isj.v8i2.41514
Rahmi, A. N., & Mikola, Y. A. (2021). Implementasi algoritma Apriori untuk menentukan pola pembelian pada customer (Studi kasus: Toko Bakoel Sembako). Information System Journal, 4(1), 14–19. https://jurnal.amikom.ac.id/index.php/infos/article/view/561
Rifania, V. S., Saniman, S., & Azlan, A. (2023). Penerapan algoritma Apriori dalam mencari pola pembelian konsumen. Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), 2(2), 201. https://doi.org/10.53513/jursi.v2i2.5750
Rosmayati, I., Wahyuningsih, W., Harahap, E. F., & Hanifah, H. S. (2023). Implementasi data mining pada penjualan kopi menggunakan algoritma Apriori. Jurnal Algoritma, 20(1), 99–107. https://doi.org/10.33364/algoritma/v.20-1.1259
Surur, M., Saputro, H., & Azizah, N. (2022). Implementasi algoritma Apriori dalam menentukan pola pembelian (Cap N Chris Café & Resto Jepara) berbasis web. Journal of Information System and Computer, 2(2), 36–45. https://doi.org/10.34001/jister.v2i2.393
Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2019). Introduction to data mining (2nd ed.). Pearson.
Jurnal ICT : Information and Communication Technologies is licensed under a 