ANALISIS SEGMENTASI PELANGGAN BERDASARKAN POLA PEMBELIAN MENGGUNAKAN METODE CLUSTERING
Keywords:
segmentasi pelanggan, klasterisasi K-Means, penambangan data, perilaku pembelianAbstract
Segmentasi pelanggan merupakan strategi penting untuk memahami keberagaman karakteristik pelanggan dan perilaku pembelian guna meningkatkan efektivitas pemasaran serta manajemen hubungan pelanggan. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan berdasarkan pola pembelian menggunakan metode K-Means clustering. Penelitian ini menggunakan dataset pelanggan publik yang berisi atribut demografis dan perilaku, termasuk usia, pendapatan tahunan, dan skor pengeluaran (spending score).Prapemrosesan data dilakukan untuk meningkatkan kualitas data, yang mencakup penanganan nilai hilang (missing values) serta normalisasi data. Jumlah klaster optimal ditentukan menggunakan Metode Elbow dan Silhouette Score, yang menghasilkan empat klaster sebagai segmentasi yang paling representatif.Hasil pengelompokan menunjukkan adanya profil pelanggan yang berbeda dengan karakteristik perilaku pembelian dan tingkat pendapatan yang beragam. Salah satu klaster merepresentasikan pelanggan dengan pendapatan tinggi dan skor pengeluaran yang tinggi, yang menunjukkan segmen pelanggan premium. Sementara itu, klaster lainnya mencerminkan pelanggan dengan tingkat pengeluaran sedang hingga rendah meskipun memiliki tingkat pendapatan yang relatif tinggi.Temuan ini menunjukkan bahwa metode K-Means clustering efektif dalam mengidentifikasi segmen pelanggan yang bermakna dan mampu mendukung pengambilan keputusan bisnis berbasis data. Model segmentasi yang diusulkan memberikan wawasan praktis dalam pengembangan strategi pemasaran yang lebih terarah serta peningkatan manajemen hubungan pelanggan dalam lingkungan bisnis digital.
Downloads
References
Abdi, H. and Williams, L.J. (2010) ‘Principal component analysis’, Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), pp. 433–459. https://doi.org/10.1002/wics.101
Barrera, F., Segura, M. and Maroto, C. (2024) ‘Multiple criteria decision support system for customer segmentation using a sorting outranking method’, Expert Systems with Applications, 238, 122310. https://doi.org/10.1016/j.eswa.2023.122310
Chen, H., Zhang, L., Chu, X. and Yan, B. (2019) ‘Smartphone customer segmentation based on the usage pattern’, Advanced Engineering Informatics, 42, 101000. https://doi.org/10.1016/j.aei.2019.101000
Christy, A.J., Umamakeswari, A., Priyatharsini, L. and Neyaa, A. (2021) ‘RFM ranking–An effective approach to customer segmentation’, Journal of King Saud University – Computer and Information Sciences, 33(10), pp. 1251–1257. https://doi.org/10.1016/j.jksuci.2018.09.004
Dong, S., Wang, P. and Abbas, K. (2021) ‘A survey on deep learning and its applications’, Computer Science Review, 40, 100379. https://doi.org/10.1016/j.cosrev.2021.100379
Joung, J. and Kim, H. (2023) ‘Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews’, International Journal of Information Management, 70, 102641. https://doi.org/10.1016/j.ijinfomgt.2023.102641
Kasem, M.S., Hamada, M. and Taj-Eddin, I. (2024) ‘Customer profiling, segmentation, and sales prediction using AI in direct marketing’, Neural Computing and Applications, 36(9), pp. 4995–5005. https://doi.org/10.1007/s00521-023-09192-5
Mukhamediev, R.I. et al. (2022) ‘Review of artificial intelligence and machine learning technologies: classification, restrictions, opportunities and challenges’, Mathematics, 10(15), 2552. https://doi.org/10.3390/math10152552
Nguyen, S.P. (2021) ‘Deep customer segmentation with applications to a Vietnamese supermarkets’ data’, Soft Computing, 25(12), pp. 7785–7793. https://doi.org/10.1007/s00500-020-05559-5
Rungruang, C. et al. (2024) ‘RFM model customer segmentation based on hierarchical approach using FCA’, Expert Systems with Applications, 237, 121449. https://doi.org/10.1016/j.eswa.2023.121449
Syakur, M.A., Khotimah, B.K., Rochman, E.M.S. and Satoto, B.D. (2018) ‘Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster’, IOP Conference Series: Materials Science and Engineering, 336(1), 012017. https://doi.org/10.1088/1757-899X/336/1/012017
Wang, C. (2022) ‘Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach’, Information Processing & Management, 59(6), 103085. https://doi.org/10.1016/j.ipm.2022.103085
Yadegaridehkordi, E. et al. (2021) ‘Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques’, Technology in Society, 65, 101528. https://doi.org/10.1016/j.techsoc.2021.101528
Dolnicar, S., Kaiser, S., Lazarevski, K. and Leisch, F. (2012)
‘Biclustering: Overcoming data dimensionality problems in market segmentation’, Journal of Travel Research, 51(1), 41–59.
https://doi.org/10.1177/0047287510394192
Likas, A., Vlassis, N. and Verbeek, J.J. (2003) ‘The global k-means clustering algorithm’, Pattern Recognition, 36(2), pp. 451–461. https://doi.org/10.1016/S0031-3203(02)00060-2
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Muhammad Ibnu Rayyan, Eka Ramadanta Sitepu, Sofy Ertika Dewi, Suci Pratiwi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
