Implementation of the Apriori Algorithm for Product Recommendation Analysis at Asyifa Serba 35.000 Retail Store in Kisaran
DOI:
https://doi.org/10.35870/ijmsit.v6i1.7289Keywords:
Data Mining, Apriori Algorithm, Product Recommendation, Sales Transaction, Retail StrategyAbstract
This study aims to implement the Apriori algorithm to analyze sales transaction data and generate product recommendations at Toko Asyifa Serba 35.000. The research addresses the problem of underutilized transaction data, where sales records are only used for administrative purposes without further analysis to support marketing strategies and decision-making. The significance of this study lies in its contribution to enhancing data-driven decision-making in retail businesses, particularly in improving product promotion strategies, inventory management, and customer satisfaction. The research adopts an applied quantitative approach with an experimental design. Data were collected through observations, interviews, and documentation of sales transactions, and analyzed using data mining techniques, specifically the Apriori algorithm, to identify frequent itemsets and association rules based on support and confidence values. The results indicate that the implementation of the Apriori algorithm successfully uncovers patterns of consumer purchasing behavior, revealing combinations of products frequently bought together. The generated recommendations provide practical benefits for retail management, including more effective product bundling strategies, optimized shelf arrangement, targeted promotional campaigns, and improved inventory planning. These improvements can contribute to increased sales opportunities and better customer shopping experiences. These findings enable the development of a recommendation system that provides accurate and relevant product suggestions. The study concludes that the application of Apriori-based recommendation systems improves sales effectiveness, optimizes product placement, and enhances customer satisfaction. It is recommended that retail businesses adopt data mining techniques to maximize the value of transaction data and further develop integrated recommendation systems for better decision support.
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Alma, E., Utami, E., & Wahyu Wibowo, F. (2020). Implementasi Algoritma Apriori untuk Rekomendasi Produk pada Toko Online Implementation of Apriori Algorithms for Product Recommendations at Online Stores. Citec Journal, 7(1), 63–74.
Amory, J. D. S., Mudo, M., & J, R. (2025). Transformasi Ekonomi Digital dan Evolusi Pola Konsumsi: Tinjauan Literatur tentang Perubahan Perilaku Belanja di Era Internet. Jurnal Minfo Polgan, 14(1), 28–37. https://doi.org/10.33395/jmp.v14i1.14608
Hanapi, A., & Sari, R. (2023). Penerapan Algoritma Apriori Untuk Rekomendasi Produk Bagi Pelanggan Toko Online Berbasis Website. Jurnal Jaring SainTek, 5(1), 51-60. https://doi.org/10.31599/4bkekg11
Hidayat, I., Askar, A., & Zaitun, Z. (2022). Teknologi Menurut Pandangan Islam. Prosiding Kajian Islam dan Integrasi Ilmu di Era Society (KIIIES) 5.0, 1(1), 456-460.
Mare, B. S., Yana, A. A., & Mandiri, U. N. (2024). Perancangan Sistem Informasi Berbasis Web Pada Oemah Kucek Laundry. Jurnal Mahasiswa Informatika Dan Desain, 2(1), 70–76. https://doi.org/10.35968/gh9ajp64
Nasrullah, A. H. (2021). Implementasi algoritma Decision Tree untuk klasifikasi produk laris. Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar, 7(2), 45-51. https://doi.org/10.35329/jiik.v7i2.203
Prasetyo, A., Sastra, R., & Musyaffa, N. (2020). Implementasi Data Mining Untuk Analisis Data Penjualan Dengan Menggunakan Algoritma Apriori (Studi Kasus Dapoerin’S). Jurnal Khatulistiwa Informatika, 8(2). https://doi.org/10.31294/jki.v8i2.8994
Putra, A. N., & Muflih, G. Z. (2024). Perancangan Sistem Informasi Perpustakaan SMA Negeri 1 Gombong Berbasis Web Menggunakan Hypertext Preprocessor (PHP) dan MySQL. Jurnal Kridatama Sains Dan Teknologi, 6(02), 522–535. https://doi.org/10.53863/kst.v6i02.1245
Rizky Mangunsong, A., Sihombing, V., & Rasyid Munthe, I. (2024). Pengembangan Sistem Rekomendasi Produk Berdasarkan Pola Pembelian dengan Pendekatan Algoritma Apriori. Jurnal Ilmu Komputer Dan Sistem Informasi (JIKOMSI), 7(1), 82–86. https://doi.org/10.55338/jikomsi.v7i1.2718
Rizmayanti, A. I., Hidayati, N., Nugraha, F. S., & Gata, W. (2021). Penerapan Data Mining Untuk Memprediksi Kompetensi Siswa Menggunakan Metode Decission Tree (Studi Kasus Smk Multicomp Depok). Swabumi, 9(1), 9–18. https://doi.org/10.31294/swabumi.v9i1.8363
Safitri, D. A. N., Halilintar, R., & Wahyuniar, L. S. (2021). Sistem Rekomendasi Skincare Menggunakan Metode Content-Based Filtering dan Algoritma Apriori. Seminar Nasional Inovasi Teknologi (SEMNAS INOTEK), 242–248. https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/1136
Srirahayu, A., & Pribadie, L. S. (2023). Review Paper Data Mining Klasifikasi Data Mining. Jurnal Ilmiah Informatika Global, 14(1). https://doi.org/10.36982/jiig.v14i1.2981
Sudarsono, B. G., Leo, M. I., Santoso, A., & Hendrawan, F. (2021). Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner. JBASE - Journal of Business and Audit Information Systems, 4(1), 13–21. https://doi.org/10.30813/jbase.v4i1.2729
Wilyanto, N., Firnando, J., Franko, B., Tanzil, S. P., Tan, H. C., & Hartati, E. (2023). Pembuatan Website Menggunakan Visual Studio Code di SMA Xaverius 3 Palembang. Fordicate, 3(1), 1–8. https://doi.org/10.35957/fordicate.v3i1.5057
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