Penerapan K-Means Clustering untuk Segmentasi Pelanggan
DOI:
https://doi.org/10.61132/uranus.v3i2.1214Keywords:
Clustering, RFM, Data Analysis, K-Means, Customer SegmentationAbstract
Public transportation, with Transjakarta as its main pillar, requires a deep understanding of customer behavior to improve service quality and maintain loyalty. This study aims to segment Transjakarta customers using data mining techniques, specifically the K-Means Clustering algorithm, based on the RFM (Recency, Frequency, Monetary/Value) behavioral model. 37,900 rows of raw transaction data were processed into a clean database, resulting in 1,917 unique customers for analysis. The RFM metrics were then normalized using Min-Max Scaler. The optimal number of clusters was evaluated using the Elbow Curve and Silhouette Score Methods, which led to the determination of k = 4 clusters. The segmentation results identified four customer groups requiring specific strategies: Cluster 3 (Champions) with high R, F, and V (requiring rewards and retention); Cluster 0 (Active, Low Value) with high R and F but low V (requiring upsells and cross-sells); Cluster 1 (Potential/At-Risk); and Cluster 2 (Dormant/Lost). Preliminary analysis (EDA) showed that nearly half of customers (49.3%) used Bank DKI cards, dominated by the productive age group (25–45 years old), with the Rusun Kapuk Muara–Penjaringan route being the busiest. The main managerial recommendation is to strengthen the partnership with Bank DKI and optimize services in this busy corridor.
References
Adiana, B. E., Soesanti, I., & Permanasari, A. E. (2018). Analisis segmentasi pelanggan menggunakan kombinasi RFM model dan teknik clustering. Jurnal Terapan Teknologi Informasi, 2(1), 23–32.
Aditya, A., Jovian, I., & Sari, B. N. (2020). Implementasi K-Means clustering ujian nasional Sekolah Menengah Pertama di Indonesia tahun 2018/2019. Jurnal Media Informatika Budidarma, 4(1), 51–58.
Alhamdani, F. D. S., Dianti, A. A., & Azhar, Y. (2021). Segmentasi pelanggan berdasarkan perilaku penggunaan kartu kredit menggunakan metode K-Means clustering. JISKA (Jurnal Informatika Sunan Kalijaga), 6(2), 70–77.
Anam, K., Rusyana, R., Nurhakim, B., & Pratama, D. (2024). Analisis tingkat penggunaan gadget pada anak usia dini dengan menggunakan K-Means. Jurnal Informatika dan Rekayasa Perangkat Lunak, 6(1), 281–288.
Harani, N. H., Prianto, C., & Nugraha, F. A. (2020). Segmentasi pelanggan produk digital service Indihome menggunakan algoritma K-Means berbasis Python. Jurnal Manajemen Informatika (JAMIKA), 10(2), 133–146.
Hardiani, T., Sulistyo, S., & Hartanto, R. (2015). Segmentasi nasabah tabungan menggunakan model RFM (Recency, Frequency, Monetary) dan K-Means pada lembaga keuangan mikro. Seminar Nasional Teknologi Informasi dan Komunikasi Terapan, 463–468.
Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327–1332.
Khobzi, H., Akhondzadeh-Noughabi, E., & Minaei-Bidgoli, B. (2014). A new application of RFM clustering for guild segmentation to mine the pattern of using banks’ e-payment services. Journal of Global Marketing, 27(3), 178–190.
Merliana, N. P. E., & Santoso, A. J. (2015). Analisa penentuan jumlah cluster terbaik pada metode K-Means clustering.
Rohman, N., & Wibowo, A. (2024). Perbandingan metode K-Medoids dan metode K-Means dalam analisis segmentasi pelanggan mall. SINTECH (Science and Information Technology) Journal, 7(1), 49–58.
Zakariyya, R. H. (2020). Customer segmentation by using RFM model and K-Means clustering in PT XYZ. Telkom University, 1–10.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
