Perancangan Sistem Customer Intelligence Berbasis Fuzzy C-Means dan Pemetaan Point of Interest (POI) untuk Mendukung Strategi Layanan Perusahaan Telekomunikasi
Abstrak
This study aims to design and develop a web-based Customer Intelligence system that integrates customer segmentation, customer value prediction, and location mapping into a unified platform. The Fuzzy C-Means algorithm is used for customer segmentation based on behavioral characteristics, while the Decision Tree Regressor is applied to predict Average Revenue per User (ARPU). In addition, the system incorporates Point of Interest (POI) visualization to support location-based analysis. The results show that the clustering model successfully identifies three to four customer segments with distinct characteristics based on ARPU, service usage, and customer loyalty. The prediction model achieves a good performance with an R² value of 0.8905 on the testing data. The system is also able to automatically generate service recommendations based on customer segmentation results. Therefore, the proposed system can assist telecommunication companies in formulating more effective and data-driven service strategies.
Referensi
Aprinia, P. R. M., & Sutanta, H. (2024). Survei dan pembuatan sistem informasi geografis alamat berkode lokasi (geocoded address) untuk wilayah Kalurahan Mantrijeron, Kota Yogyakarta. JGISE: Journal of Geospatial Information Science and Engineering, 7(2). https://doi.org/10.22146/jgise.102071
Diyono. (2024). Pengembangan sistem informasi lokasi kejahatan jalanan di Daerah Istimewa Yogyakarta. JGISE: Journal of Geospatial Information Science and Engineering, 7(1). https://doi.org/10.22146/jgise.91254
Dwitiyanti, N., Kumala, S. A., & Handayani, S. D. (2024). Comparative study of earthquake clustering in Indonesia using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP algorithms. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 8(6). https://doi.org/10.29207/resti.v8i6.5514
Fitrah, F. J., Fadlil, A., & Umar, R. (2023). Analysis of the Saintekmu website quality on user satisfaction using the modified System Usability Scale and WebQual 4.0 method. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(6). https://doi.org/10.29207/resti.v7i6.5116
Nurhidayat, M. M. S., & Anggraini, D. (2023). Analysis and classification of customer churn using machine learning models. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(6), 1253–1259. https://doi.org/10.29207/resti.v7i6.4933
Setyadi, W., Nurhadryani, Y., & Hermadi, I. (2024). Pengembangan sistem manajemen data spasial aset jalan tol: Studi kasus ruas Jalan Tol Bakauheni–Terbanggi Besar. JGISE: Journal of Geospatial Information Science and Engineering, 7(1). https://doi.org/10.22146/jgise.97651
Reinartz, W., Krafft, M., & Hoyer, W. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41(3), 293–305. https://doi.org/10.1509/jmkr.41.3.293.35991
Sokol, O., & Holý, V. (2019). The role of shopping mission in retail customer segmentation. https://arxiv.org/abs/1909.02996
John, J. M., Shobayo, O., Ogunleye, B., et al. (2024). An exploration of clustering algorithms for customer segmentation in the UK retail market. https://arxiv.org/abs/2402.04103
Nguyen, T. D., et al. (2023). Retail store customer behavior analysis system: Design and implementation. arxiv.org/abs/2309.03232
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