Implementation of K-Medoids Clustering Method for B2B Service Price Segmentation at PT Telkom Tasikmalaya - Elsa Amalinda; Andi Nur Rachman; Cecep Muhammad Sidik Ramdani (Journal of Computer Networks, Architecture and High Performance Computing, 2026)
PT Telkom Indonesia Witel Tasikmalaya faces strategic challenges in monitoring customer behavior in real-time and determining optimal Business-to-Business (B2B) service pricing strategies due to conventional data management. This study aims to design a Web-based Decision Support System to analyze B2B service pricing using the K-Medoids clustering algorithm with Manhattan Distance metrics. The research and system development are fully structured using the Agile methodology. The model evaluation is measured using the Davies-Bouldin Index. The data mining modeling indicates that the formation of three clusters is the most optimal partition with the smallest Davies-Bouldin Index value of 0.638. The clusters successfully map customer profiles from small enterprises to large corporations as a baseline price recommendation. The system is built using an implementation of vanilla JavaScript and Google Firebase serverless architecture, achieving success in functional black-box testing and a 90% score in user acceptance testing. This research provides a strategic contribution by accelerating the issuance of quotation documents and optimizing company revenue.