Market Basket Analysis Using FP-Growth and Apriori on Distro Store Sales Transaction

Umi Meganinditya Wulandari; Akrim Teguh Suseno; Muhammad Kholilurrahman

doi:10.18860/mat.v17i1.28820

Market Basket Analysis Using FP-Growth and Apriori on Distro Store Sales Transaction

Umi Meganinditya Wulandari, Akrim Teguh Suseno, Muhammad Kholilurrahman

Abstract

Market Basket Analysis analyzes consumer buying habits by finding relationships between items in the consumer's shopping basket. This Market Basket Analysis can provide success to the retail industry with the ability to understand consumer behavior and the speed of response to information obtained by retail business owners. This understanding is the result of an analysis that can help business owners improve marketing and sales strategies while utilizing transaction data. Sales transaction data that has been accumulated so far has only become data warehouses, while large amounts of transaction data can bring major changes to the level of competition in business and business actors in order to survive in the business world. In addition, after the COVID-19 outbreak, Indonesia experienced a slowdown in economic growth of 5.31%. This can be overcome by utilizing Market Basket Analysis to increase sales from their businesses. MBA with the methods used are FP-Growth and Apriori to analyze store transaction data in order to obtain association rules that can be used in improving marketing strategies. This analysis was carried out with 3 scenarios for 3 different minimum support values (1%, 2% and 3%) but the same minimum confidence value of 0.6 (60%). The comparison of the two methods is that 2 out of 3 scenarios produce the same association rule, namely 1 final association rule result with a lift value of 1.42. The three scenario results from both methods can be used by business owners as a consideration in determining sales strategies.

Keywords

Market Basket Analysis, FP-Growth, Apriori, Association Rules, Marketing Strategy

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References

[1] M. H. Santoso, “Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom,” Brilliance: Research of Artificial Intelligence, vol. 1, no. 2, pp. 54–66, 2021, doi: 10.47709/brilliance.v1i2.1228.

[2] A. R. Efrat, R. Gernowo, and Farikhin, “Consumer purchase patterns based on market basket analysis using apriori algorithms,” J Phys Conf Ser, vol. 1524, no. 1, 2020, doi: 10.1088/1742-6596/1524/1/012109.

[3] A. T. Suseno, A. R. Naufal, and M. Al Amin, “Market Based Analysis Sebagai Peningkatan Penjualan Produk Menggunakan Algoritma K-Medoids Dan Fp-Growth,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 5, no. 2, p. 301, 2022, doi: 10.37600/tekinkom.v5i2.646.

[4] L. Samboteng, Rulinawaty, M. R. Kasmad, M. Basit, and R. Rahim, “Market Basket Analysis of Administrative Patterns Data of Consumer Purchases Using Data Mining Technology,” Journal of Applied Engineering Science, vol. 20, no. 2, pp. 339–345, 2022, doi: 10.5937/jaes0-32019.

[5] M. Kholilurrahman, W. A. Syafei, and O. D. Nurhayati, “Klasifikasi Image Processing Pada Citra Warna Daun Padi Menggunakan Metode Convolutional Neural Network,” Jurnal Ilmiah Sains, vol. 23, no. 2, pp. 175–186, 2023, doi: 10.35799/jis.v23i2.50415.

[6] U. M. Wulandari, B. Warsito, and F. Farikin, “Survival Information System Using ReliefF Feature Selection and Backpropagation in Hepatocellular Carcinoma Disease,” 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding, pp. 37–42, 2023, doi: 10.1109/ISITIA59021.2023.10221079.

[7] S. K. Dubey, S. Mittal, S. Chattani, and V. K. Shukla, “Comparative Analysis of Market Basket Analysis through Data Mining Techniques,” Proceedings of 2nd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021, pp. 239–243, 2021, doi: 10.1109/ICCIKE51210.2021.9410737.

[8] R. Xu, “the Evaluation of Ethnic Costume Courses Based on Fp-Growth Algorithm,” Scalable Computing, vol. 25, no. 1, pp. 313–326, 2024, doi: 10.12694/scpe.v25i1.2297.

[9] D. WICAKSONO, M. I. JAMBAK, and D. M. SAPUTRA, “The Comparison of Apriori Algorithm with Preprocessing and FP-Growth Algorithm for Finding Frequent Data Pattern in Association Rule,” vol. 172, no. Siconian 2019, pp. 315–319, 2020, doi: 10.2991/aisr.k.200424.047.

[10] B. S. Pranata and D. P. Utomo, “Penerapan Data Mining Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel Motor (Study Kasus Bengkel Sinar Service),” Bulletin of Information Technology (BIT), vol. 1, no. 2, pp. 83–91, 2020.

[11] A. I. Idris et al., “Comparison of Apriori, Apriori-TID and FP-Growth Algorithms in Market Basket Analysis at Grocery Stores,” The IJICS (International Journal of Informatics and Computer Science), vol. 6, no. 2, p. 107, 2022, doi: 10.30865/ijics.v6i2.4535.

[12] F. Firmansyah and A. Yulianto, “Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta,” Journal of Informatics and Telecommunication Engineering, vol. 4, no. 2, pp. 383–392, 2021, doi: 10.31289/jite.v4i2.4539.

[13] S. Anas, N. Rumui, A. Roy, and P. H. Saputro, “Comparison of Apriori Algorithm and FP-Growth in Managing Store Transaction Data,” International Journal of Computer and Information System (IJCIS), vol. 3, no. 4, pp. 158–162, 2022, doi: 10.29040/ijcis.v3i4.96.

[14] T. Kurniana, A. Lestari, and E. D. Oktaviyani, “Penerapan Algoritma Apriori untuk Mencari Pola Transaksi Penjualan Berbasis Web pada Cafe Sakuyan Side,” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 3, no. 1, pp. 13–23, 2023, doi: 10.24002/konstelasi.v3i1.7005.

[15] F. Masya, A. Fathurrozi, and Sugiyatno, “Implementasi Algoritma Apriori Untuk Prediksi Transaksi Penjualan Produk Pada Aplikasi Point Of Sales,” Technomedia Journal, vol. 8, no. 2, pp. 70–81, 2023, doi: 10.33050/tmj.v8i2.2004.

[16] W. P. Nurmayanti et al., “Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data,” International Journal of Educational Research & Social Sciences, vol. 2, no. 1, pp. 132–139, 2021, doi: 10.51601/ijersc.v2i1.45.

[17] Y. Yang, N. Tian, Y. Wang, and Z. Yuan, “A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data,” International Journal of Computers, Communications and Control, vol. 17, no. 4, 2022, doi: 10.15837/ijccc.2022.4.4806.

DOI: https://doi.org/10.18860/mat.v17i1.28820

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