PENERAPAN ALGORITMA FUZZY C-MEANS UNTUK SEGMENTASI PELANGGAN BERBASIS MODEL RFM PADA DATA TRANSAKSI E-COMMERCE

Dimas Pramudya; Nana Suarna; Agus Bahtiar; Indra Wiguna Marthanu; Kaslani .

Authors

Dimas Pramudya STMIK IKMI CIREBON, Indonesia
Nana Suarna STMIK IKMI Cirebon, Indonesia
Agus Bahtiar STMIK IKMI Cirebon, Indonesia
Indra Wiguna Marthanu STMIK IKMI Cirebon, Indonesia
Kaslani . STMIK IKMI Cirebon, Indonesia

Keywords:

Clustering, Segmentasi Pelanggan, K-Means, Strategi Layanan, Loyalitas Pelanggan, rfm, fuzzy c-means, data mining, e-commerce

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan model segmentasi pelanggan e-commerce menggunakan integrasi metode Recency, Frequency, Monetary (RFM) dan algoritma Fuzzy C-Means (FCM). Metode RFM digunakan untuk mengevaluasi perilaku pelanggan berdasarkan waktu pembelian terakhir, frekuensi transaksi, dan total nilai pembelian, sedangkan FCM diaplikasikan untuk melakukan segmentasi berdasarkan keanggotaan fuzzy yang mencerminkan derajat keterlibatan pelanggan dalam setiap klaster. Prosedur penelitian meliputi pemuatan dan pembersihan data transaksi, perhitungan skor RFM, normalisasi data menggunakan Min-Max scaling, evaluasi jumlah klaster menggunakan indeks Davies-Bouldin dan Xie-Beni, serta implementasi algoritma FCM. Hasil penelitian menunjukkan bahwa lima klaster optimal dapat mengelompokkan pelanggan ke dalam segmen yang representatif, seperti pelanggan loyal, potensial, dan dorman. Visualisasi hasil menunjukkan distribusi yang jelas antar segmen dan membantu dalam mengidentifikasi strategi retensi pelanggan yang lebih efektif. Model ini memberikan pendekatan adaptif berbasis data yang relevan dengan dinamika perilaku pelanggan digital masa kini, serta mendukung pengambilan keputusan bisnis yang lebih tepat sasaran.

References

Abednego, L., Nugraheni, C. E., & Salsabina, A. (2023). Customer segmentation: Transformation from data to marketing strategy BT - IAIC SEMNASTIK 2023 (Vol. 4, Issue 1, pp. 139–152). https://doi.org/10.34306/conferenceseries.v4i1.645

Alsmadi, A. A., Shuhaiber, A., Al Okaily, M., Al Gasaymeh, A., & Alrawashdeh, N. (2024). Big data analytics and innovation in e-commerce: current insights and future directions. Journal of Financial Services Marketing, 29(4), 1635–1652. https://doi.org/10.1057/s41264-023-00235-7

Alves Gomes, M. A., & Meisen, T. (2023). A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. Information Systems and E-Business Management, 21(3), 527–570. https://doi.org/10.1007/s10257-023-00640-4

Beiranvand, B. (2024). Comparison of K-means and Fuzzy C-Means algorithms to optimize clustering results. International Journal of Data Science and Analytics. https://doi.org/10.1016/j.jdsa.2024.00000

Chang, S.-C., Chuang, W.-C., & Jeng, J.-T. (2023). New Interval Improved Fuzzy Partitions Fuzzy C-Means Clustering Algorithms under Different Distance Measures for Symbolic Interval Data Analysis. Applied Sciences, 13(22), 12531. https://doi.org/10.3390/app132212531

Griva, A., Papaioannou, E., Kokkinis, A., & Konstantoulaki, K. (2024). A two-stage business analytics approach to perform behavioural and geographic customer segmentation using e-commerce delivery data. Journal of Marketing Analytics, 12(1), 41–59.

Handojo, A., Pujawan, N., Santosa, B., & Singgih, M. L. (2023). A multi-layer recency-frequency-monetary method for customer priority segmentation in online transaction. Cogent Engineering, 10(1). https://doi.org/10.1080/23311916.2022.2162679

Ho, T., Nguyen, S., Nguyen, H., Nguyen, N., Man, D. S., & Le, T. G. (2023). An extended RFM model for customer behaviour and demographic analysis in the retail industry. Business Systems Research, 14(1), 26–53. https://doi.org/10.2478/bsrj-2023-000227

Ifrim, A. M. (2025). A Hybrid Numerical–Semantic Clustering Algorithm Based on RFM and Semantic Data. Algorithms, 18(10). https://doi.org/10.3390/a18100607

Krasnov, D., Davis, D., Malott, K., Chen, Y., Shi, X., & Wong, A. F. (2023). Fuzzy CMeans Clustering: A Review of Applications in Breast Cancer Detection. Entropy, 25(7). https://doi.org/10.3390/e25071021

Marín Díaz, G. (2025). A Fuzzy XAI Framework for Customer Segmentation and Risk Detection: Integrating RFM, 2 Tuple Modeling, and Strategic Scoring. Mathematics, 13(13), 2141. https://doi.org/10.3390/math13132141

Pérez‑Ortega, J. (2024). Hybrid Fuzzy C‑Means Clustering Algorithm, improving solution quality and reducing computational complexity. Axioms, 13(9), 592. https://doi.org/10.3390/axioms13090592

Putra, M. I. J., Alexander, V., Chusyairi, A., & Abdurrahman, R. A. (2025). Enhancing e‑commerce customer segmentation with fuzzy C‑means soft clustering probabilities. Journal of Artificial Intelligence and Computational Intelligence, 9(5). https://doi.org/10.30871/jaic.v9i5.10652

Rungruang, C., Riyapan, P., Intarasit, A., & Muangprathub, J. (2024). RFM model customer segmentation based on hierarchical approach using formal concept analysis. Expert Systems with Applications, 237, 121449. https://doi.org/10.1016/j.eswa.2023.121449

Smaili, M. Y., & Hachimi, H. (2023). New RFM D classification model for improving customer analysis and response prediction. Ain Shams Engineering Journal, 14, 102254. https://doi.org/10.1016/j.asej.2023.102254

Song, C. (2024). Performance study of fuzzy C‑means clustering algorithm in discrete data processing. Journal of Intelligent & Fuzzy Systems, 47(3), 3211–3223. https://doi.org/10.3233/JIFS-235082

Syahra, Y., Fadlil, A., & Yuliansyah, H. (2025). Customer Segmentation Using RFM and KMeans: Preprocessing with MinMax Normalization. Sinkron: Jurnal Teknik Informatika, 8(1). https://doi.org/10.14710/sinkron.v8i1.14907

Uddin, M. A., Ahmed, R., & Alam, S. (2024). Data-driven strategies for digital native markets: consumer insights and segmentation in online retail. Journal of Retailing and Consumer Services, 71, 103800. https://doi.org/10.1016/j.jretconser.2024.103800

Wang, C., Zhou, M.-C., Pedrycz, W., & Li, Z. W. (2024). Comparative study on noise‑estimation‑based fuzzy C‑means clustering for image segmentation. IEEE Transactions on Cybernetics, 54(1), 241–253. https://doi.org/10.1109/TCYB.2022.3217897

Wasilewski, A., Juszczyszyn, K., & Suryani, V. (2024). Multi-factor evaluation of clustering methods for e-commerce customer segmentation. Egyptian Informatics Journal, 28, 100562. https://doi.org/10.1016/j.eij.2024.100562

Zhou, K., & Yang, S. (2025). Intelligent customer segmentation: unveiling consumer patterns with RFM and fuzzy Cmeans clustering. https://doi.org/[DOI pending]