Potential Wi-Fi Customer Segmentation System Based on K-Means and WebGIS
DOI:
https://doi.org/10.57152/malcom.v6i4.2911Keywords:
Customer Segmentation, K-Means, Wi-Fi, Webgis, Web-Based SystemAbstract
Identifying potential IndiBiz Wi-Fi customers at PT Telkom Indonesia Pemalang Area requires a structured, measurable approach, as customer data is distributed across multiple geographic locations and is difficult to prioritize manually. In the case study, the initial dataset comprised 5,070 Point of Interest (POI) records, indicating that manual verification would be inefficient if all prospective customers were given the same priority. This study proposes a web-based segmentation system that integrates K-Means clustering, Optical Distribution Point (ODP) association analysis, WebGIS visualization, and Scrum-based system development into a single operational platform. The system uses Point of Interest (POI) data, including longitude, latitude, and odp_count, as clustering features to represent spatial location and infrastructure associations. The initial dataset consisted of 5,070 records and 12 attributes. After preprocessing, 5,001 valid records were used for clustering. The evaluation results showed that the best segmentation was obtained using K=3, with a silhouette score of 0.4833 and a Davies-Bouldin Index of 0.8116. The clustering results produced 2,771 medium-potential records, 1,525 low-potential records, and 705 high-potential records. The high-potential segment accounts for 14.10% of valid customer candidates and can serve as an initial priority list for field verification.
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References
Badan Pusat Statistik, “Statistik Telekomunikasi Indonesia 2024,” Badan Pusat Statistik, 2025.
PT Telkom Indonesia Tbk, “Business,” Telkom Indonesia.
Y. Putri, D. Aldo, and W. Ilham, “Retail marketing strategy optimization: Customer segmentation with artificial intelligence integration and K-Means clustering,” Sinkron: Jurnal dan Penelitian Teknik Informatika, vol. 8, no. 4, 2024, doi: 10.33395/sinkron.v8i4.14000.
F. A. Karima and A. M. Shiddiqi, “Optimization of access point positioning on Wi-Fi networks using the K-Means clustering method,” IPTEK The Journal for Technology and Science, vol. 33, no. 1, pp. 13–25, 2025.
B. Susetyo, E. Purwaningsih, W. Sutriani, E. Purnamasari, and M. Bagus, “Utilization of WebGIS for visualization of the distribution of tourist destination religious objects in Nagari Batuhampar of Lima Puluh Kota Regency, West Sumatera Province,” JOIV: International Journal on Informatics Visualization, vol. 8, no. 2, pp. 854–861, 2024, doi: 10.62527/joiv.8.2.2281.
I. Salam, N. A. Ramdhan, and A. Khamid, “Pengembangan sistem informasi geografis berbasis web untuk pemetaan partisipasi pemilu menggunakan algoritma K-Means di Kabupaten Brebes,” JUTECH: Journal Education and Technology, vol. 6, no. 2, 2025, doi: 10.31932/jutech.v6i2.5200.
R. Wandri, M. R. Fadhilah, P. R. Setiawan, and M. Fadhilla, “Agile Scrum as a development approach: A case study of web-based school information system design,” Sistemasi, vol. 14, no. 4, 2025.
P. Koukaras and C. Tjortjis, “Data preprocessing and feature engineering for data mining: Techniques, tools, and best practices,” AI, vol. 6, no. 10, p. 257, 2025, doi: 10.3390/ai6100257.
M. M. Ahsan, M. A. P. Mahmud, P. K. Saha, K. D. Gupta, and Z. Siddique, “Effect of data scaling methods on machine learning algorithms and model performance,” Technologies, vol. 9, no. 3, p. 52, 2021, doi: 10.3390/technologies9030052.
K. P. Sinaga and M. S. Yang, “Unsupervised K-means clustering algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/ACCESS.2020.2988796.
K. Muttaqin, R. Nurhidayah, N. Novianda, A. Ihsan, J. Sultan, and F. Rifqiyah, “Implementation of K-Means clustering in mapping teacher distribution using geographic information system,” Elinvo: Electronics, Informatics, and Vocational Education, vol. 9, no. 1, pp. 187–196, 2024, doi: 10.21831/elinvo.v9i1.76884.
Y. Hasan, “Pengukuran silhouette score dan Davies-Bouldin Index pada hasil cluster K-Means dan DBSCAN,” KAKIFIKOM: Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer, vol. 6, no. 1, pp. 60–74, 2024.
M. Pastrana, H. Ordoñez, C. A. Cobos-Lozada, and M. Muñoz, “Best practices evidenced for software development based on DevOps and Scrum: A literature review,” Applied Sciences, vol. 15, no. 10, p. 5421, 2025, doi: 10.3390/app15105421.
R. Renaldi and L. Damayanti, “Design and validation of a web-based E-CRM system for baby fashion business: A case study of PT Rhinno Makmur Jaya using black box and UAT testing,” Journal of Artificial Intelligence and Engineering Applications, vol. 4, no. 3, pp. 1994–1998, 2025, doi: 10.59934/jaiea.v4i3.1071.
G. A. Winasis, E. Lutfina, and G. W. Saraswati, “Integration of chatbot and complaint website using Agile Scrum with load testing and UAT,” Sinkron: Jurnal dan Penelitian Teknik Informatika, vol. 10, no. 2, 2026, doi: 10.33395/sinkron.v10i2.15879.
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Copyright (c) 2026 Naia Aulia, Muhamad Azrino Gustalika, Dany Candra Febrianto
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