Customer-Centric Retail Analytics Using RFM Segmentation and Segment-Level FP-Growth for Product Recommendation

Muhammad Tegar Juleo Wicaksana Juleo; Anteng Widodo; Andy Prasetyo Utomo

doi:10.30865/json.v8i1.10090

Authors

Muhammad Tegar Juleo Wicaksana Juleo Universitas Muria Kudus
Anteng Widodo Universitas Muria Kudus
Andy Prasetyo Utomo Universitas Muria Kudus

DOI:

https://doi.org/10.30865/json.v8i1.10090

Keywords:

Retail Analytics, RFM, K-Means, FP-Growth, Product Bundles

Abstract

Retail transaction data are often used only for sales recording, leaving customer value and product affinity disconnected from managerial decisions. This study develops a web-based retail analytics framework that integrates RFM, K-Means, and global and segment-level FP-Growth to generate product-bundle, availability-check, and shelf-placement recommendations for SRC Inka Jaya. The analysis used 198,879 non-tobacco transaction lines representing 67,284 invoices, 2,565 products, and 1,257 identified customers from 1 January 2025 to 22 June 2026. RFM variables were transformed using , standardized, and evaluated for k=2–6. A four-cluster solution was selected as a trade-off between statistical quality and managerial usefulness, with a silhouette score of 0.371 and a Davies-Bouldin index of 0.934. Champion customers represented 28.48% of identified customers but contributed 54.35% of their monetary value. The primary FP-Growth scenario produced 963 rules, including 188 global and 775 segment-level rules, consolidated into 86 bundles, 46 availability-priority products, and 69 shelf-placement recommendations. Temporal validation achieved a 60.61% rule-match hit rate. Combining store-wide and segment-specific patterns produced more contextual recommendations and supported traceable delivery through an active-dataset dashboard.

References

[1] P. Salsa Nabila, A. Indah Lestari Nasution, and M. Ikhsan Harahap, “Analisis Efektivitas Inovasi Digital SRC (Sampoerna Retail Community) Bagi Toko Kelontong Di Kecamatan Kuala Kabupaten Langkat,” Jurnal Media Wahana Ekonomika, vol. 20, pp. 355–366, Jul. 2023, [Online]. Available: https://jurnal.univpgri-palembang.ac.id/index.php/Ekonomika/index

[2] A. Widodo and A. Abdul Chamid, “Peramalan Penjualan Genteng Menggunakan Metode Holt-Winters Exponential Smoothing,” Jurnal SITECH: Sistem Informasi dan Teknologi, vol. 8, no. 2, pp. 291–302, 2025, [Online]. Available: https://jurnal.umk.ac.id/index.php/sitech/article/view/16765/6843

[3] S. F. Djun, I. G. A. Gunadi, and S. Sariyasa, “Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM,” JTIM : Jurnal Teknologi Informasi dan Multimedia, vol. 5, no. 4, pp. 354–364, Feb. 2024, doi: 10.35746/jtim.v5i4.434.

[4] A. L. M. Tampubolon, T. M. E. Y. Butar Butar, and S. Rochimah, “Segmentasi Pelanggan Majalah pada Situs Web E-Commerce dengan K-Means++ dan Metode RFM,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 6, pp. 1243–1252, Dec. 2024, doi: 10.25126/jtiik.2024118208.

[5] R. M. Fauzan and G. Alfian, “Segmentasi Pelanggan E-Commerce Menggunakan Fitur Recency, Frequency, Monetary (RFM) dan Algoritma Klasterisasi K-Means,” JISKA (Jurnal Informatika Sunan Kalijaga), vol. 9, no. 3, pp. 170–177, Sep. 2024.

[6] A. Yusak, N. Rumapea, D. Pratiwi, and S. Sari, “Analisis Segmentasi Pelanggan Ritel Online Menggunakan K-Means Clustering Berdasarkan Model Recency, Frequency, Monetary (RFM),” Jurnal Sains dan Teknologi, vol. 6, no. 3, pp. 292–299, 2024, doi: 10.55338/saintek.v6i3.4607.

[7] M. F. Fadhillah, A. S. A. Lovely, and I. Puspitasari, “Customer Segmentation with Clustering Algorithm Based on Recency, Frequency, and Monetary (RFM) Attributes,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 1, pp. 48–56, Jan. 2025, doi: 10.57152/malcom.v5v1.1491.

[8] D. A. Imanuel and G. Alfian, “Visualisasi Segmentasi Pelanggan Berdasarkan Atribut RFM Menggunakan Algoritma K-Means Untuk Memahami Karakteristik Pelanggan pada Toko Retail Online,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 12, no. 2, pp. 283–292, Apr. 2025, doi: 10.25126/jtiik.2025128619.

[9] A. Syaharani, V. Ilhadi, and A. Karima, “ANALISIS CLUSTERING SEGMENTASI PELANGGAN PADA CV PRIMA JAYA UTAMA MENGGUNAKAN METODE K-MEANS,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 5, Oct. 2025.

[10] I. Iqbal, N. Hidayat, D. P. Gevano, and A. P. R. Ilahi, “Segmentasi Pelanggan Menggunakan K-Means Clustering Berdasarkan Data Kepribadian dan Pola Konsumsi,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 5, pp. 3914–3924, Oct. 2025, doi: 10.52436/1.jutif.2025.6.5.5140.

[11] E. W. Agustin, K. Uthami, A. I. Ulfa, L. Efrizoni, and R. Rahmaddeni, “Optimization of Customer Segmentation in the Retail Industry Using the K-Medoid Algorithm,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 3, pp. 766–775, Jun. 2025, doi: 10.57152/malcom.v5i3.1977.

[12] Y. Syahra, A. Fadlil, and H. Yuliansyah, “Customer Segmentation Using RFM and K- Means Clustering to Support CRM in Retail Industry,” Sinkron : Jurnal dan Penelitian Teknik Informatika, vol. 9, no. 3, pp. 1108–1119, Jul. 2025, doi: 10.33395/sinkron.v9i3.14974.

[13] I. Musdalifah and A. Jananto, “Analisis Perbandingan Algoritma Apriori Dan FP-Growth Dalam Pembentukan Pola Asosiasi Keranjang Belanja Pelanggan,” Jurnal Ilmiah Komputer, vol. 18, pp. 175–184, Aug. 2022.

[14] S. R. Karimah and E. D. Udayanti, “Comparison of Apriori and FP-Growth Algorithms in Market Basket Analysis for Online Book Sales,” Journal of Applied Informatics and Computing (JAIC), vol. 10, no. 1, pp. 566–574, Feb. 2026, [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC

[15] U. M. Wulandari, A. T. Suseno, and M. Kholilurrahman, “Market Basket Analysis Using FP-Growth and Apriori on Distro Store Sales Transaction,” MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), vol. 17, no. 1, pp. 12–18, Mar. 2025, doi: 10.18860/mat.v17i1.28820.

[16] H. Hairani and J. Ximenes Guterres, “Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data,” International Journal of Engineering and Computer Science Applications (IJECSA), vol. 3, no. 1, pp. 33–42, Mar. 2024, doi: 10.30812/ijecsa.v3i1.3874.

[17] F. Achmad, O. Nurdiawan, and Y. A. Wijaya, “ANALISA POLA TRANSAKSI PEMBELIAN KONSUMEN PADA TOKO RITEL KESEHATAN MENGGUNAKAN ALGORITMA FP-GROWTH,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 1, pp. 168–175, Feb. 2023, doi: https://doi.org/10.36040/jati.v7i1.6210.

[18] V. M. Christy and I. Maryati, “FP-Growth Implementation for Market Basket Analysis in Building Materials Store in Surabaya,” Journal of Applied Informatics and Computing (JAIC), vol. 10, no. 2, pp. 1751–1759, Apr. 2026, [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC

[19] N. Purwati and S. Karnila, “Strategi Peningkatan Penjualan Produk Menggunakan Market Basket Analysis,” Jurnal Sistem Informasi Bisnis, vol. 13, no. 2, pp. 96–103, Oct. 2023, doi: 10.21456/vol13iss2pp96-103.

[20] I. Ismarmiaty and R. Rismayati, “Product Sales Promotion Recommendation Strategy with Purchase Pattern Analysis FP-Growth Algorithm,” Sinkron : Jurnal dan Penelitian Teknik Informatika, vol. 7, no. 1, pp. 202–211, Jan. 2023, doi: 10.33395/sinkron.v8i1.11898.

[21] D. Alfitra, M. Afdal, M. Fronita, and E. Saputra, “Analisa Keranjang Belanja untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth,” Sistemasi: Jurnal Sistem Informasi, vol. 13, no. 4, pp. 1651–1661, Jul. 2024, [Online]. Available: http://sistemasi.ftik.unisi.ac.id

[22] C. Olyfiyany and H. Risma Ameilya, “Segmentasi Pelanggan Berbasis RFM dan Analisis Asosiasi Produk pada Olist Brazilian E-Commerce Menggunakan FP-Growth,” Journal of Information Systems and Business Technology, vol. 2, pp. 910–917, 2026, [Online]. Available: https://journal.jci.co.id/jisbt

[23] S. Aulia, A. N. Muhammad, and A. Wibowo, “Optimization of Customer Segmentation with RFM, K-Means, and FP- Growth for Marketing Strategy,” SINTECH JOURNAL, vol. 8, pp. 163–177, Aug. 2025, doi: https://doi.org/10.31598/sintechjournal.v8i2.1942.

[24] K. F. Handoko, N. Nugraha, and A. Sujjada, “Customer Segmentation-Based Promotion Recommendation Using RFM and K-Means Clustering on a Web Platform,” bit-Tech, vol. 8, no. 3, pp. 3661–3672, Apr. 2026, doi: 10.32877/bt.v8i3.3677.

[25] S. A. Alfatikhah, S. Supriyono, and S. Adiyono, “IMPLEMENTASI METODE FP-GROWTH DALAM ANALISA POLA PEMBELIAN PELANGGAN PADA SWALAYAN PANTES PATI BERBASIS WEB,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 11, no. 1, pp. 1535–1545, Jan. 2026, doi: 10.36341/rabit.v11i1.7401.

[26] N. A. Khumaira, D. Priyanto, H. Hairani, G. H. Martono, Moch. Syahrir, and H. Husain, “Analisis Pola Pembelian Konsumen Menggunakan Algoritma FP-Growth pada Data Transaksi Restaurant Burger,” Jurnal Teknologi Informasi dan Multimedia, vol. 8, no. 3, pp. 497–509, Jul. 2026, doi: 10.35746/jtim.v8i3.983.