Analisis Association Rule Menggunakan Algoritma FP-Growth pada Dataset Online Retail
DOI:
https://doi.org/10.63822/5ap2mr81Keywords:
Association Rule Mining; FP-Growth; Market Basket Analysis; Online Retail Dataset; Retail TransactionAbstract
Market Basket Analysis is widely used to identify consumer purchasing patterns from transaction data and support data-driven business decisions. This study aims to analyze product purchase patterns using the FP-Growth algorithm on the Online Retail Dataset obtained from the UCI Machine Learning Repository. The research employed a data preprocessing stage, including removing missing values, duplicate records, cancelled transactions, and invalid transaction values, followed by transaction transformation and one-hot encoding. The FP-Growth algorithm was implemented with a minimum support of 1%, while association rules were generated using a minimum confidence of 50%. The results produced 973 frequent itemsets, indicating that the algorithm effectively identified frequently purchased product combinations. The strongest association rule was found between REGENCY TEA PLATE PINK and REGENCY TEA PLATE GREEN, with a support value of 0.0109, a confidence value of 90.18%, and a lift value of 61.90. These findings demonstrate that FP-Growth is effective in discovering purchasing patterns and can support cross-selling, product bundling, shelf arrangement, and inventory management strategies in the retail sector.
References
Al-Etaibi, B., Ahmad, A., Al‐Naymat, G., & Al-Betar, M. A. (2025). Data-Driven Sales Prediction and Strategic Insights for E-Commerce Through ML and BI Tools. 152–157. https://doi.org/10.1109/itt69610.2025.11352921
Amelia, R., Darmansyah, D., & Rismadin, A. M. (2024). Perbandingan Algoritma Apriori dan Fp-Growth dalam Pengaplikasian Market Basket Analysis untuk Strategi Bisnis Retail. Building of Informatics, Technology and Science (BITS), 6(1). https://doi.org/10.47065/bits.v6i1.5388
Amsury, F., Kurniawati, I., & Fahdia, M. R. (2023). Implementasi association rules menentukan pola pemilihan menu di the gade coffee & gold menggunakan algoritma apriori. Infotech Journal, 9(1), 279–286. https://doi.org/10.31949/infotech.v9i1.5357
Chandanan, A. K., & Shukla, M. (n.d.). Association Rule Mining Using Hbpso. https://doi.org/10.21172/1.841.06
Dwiputra, D., Widodo, A. M., Akbar, H., & Firmansyah, G. (2023). Evaluating the Performance of Association Rules in Apriori and FP-Growth Algorithms: Market Basket Analysis to Discover Rules of Item Combinations. Journal of World Science. https://doi.org/10.58344/jws.v2i8.403
Harini, J. V., Venu, G., Reddy, G. M. S., Datta, B., Goud, M. V., Mohammed, T. K., Mohammed, T. K., & Ashok, Prof. (2024). Market Basket Analysis. International Journal For Science Technology And Engineering, 12(5), 5224–5229. https://doi.org/10.22214/ijraset.2024.61662
Hermawan, A., Wicaksono, B., Ahmadjayadi, T., Prakasa, B. S., & Dacomoro, J. (2024). Implementasi Algoritma Apriori pada Market Basket Analysis terhadap Data Penjualan Produk Supermarket. https://doi.org/10.62383/algoritma.v2i5.137
Islamiyah, P. L., Ginting, N. D., & Taruk, M. (2019). Comparison of Priori and FP-Growth Algorithms in Determining Association Rules. https://doi.org/10.1109/ICEEIE47180.2019.8981438
Sagin, A. N., & Ayvaz, B. (2018). Determination of Association Rules with Market Basket Analysis: Application in the Retail Sector. 7(1). https://doi.org/10.21533/SCJOURNAL.V7I1.149
Sahare, R. (2024). Sales Data Analysis Using BI and WEKA. Indian Scientific Journal Of Research In Engineering And Management. https://doi.org/10.55041/ijsrem30123
Siregar, M. N. H., Khalidy, F., Rismayanti, R., & Khairunnisa, K. (2024). Utilization of Sales Data Analysis for Product Recommendation Systems in E-Commerce Using the Apriori Algorithm. 1(2), 41–45. https://doi.org/10.64803/jocsaic.v1i2.17
Wijaya, A. Y., Faqih, A., Solihudin, D., Rohmat, C. L., & Permana, S. E. (2024). Penerapan association rules menggunakan algoritma apriori untuk identifikasi pola pembelian. JATI (Jurnal Mahasiswa Teknik Informatika), 7(6), 3871–3878. https://doi.org/10.36040/jati.v7i6.8270
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