Decision Tree-Based Predictive Model Development for RumahNet Customer Satisfaction Analysis in West Jakarta - Yuliantoro, Dita Tri; Sarimole, Frencis Matheos (Journal Innovations Computer Science, 2025)
The rapid growth of information technology has amplified the demand for fast and reliable internet services, particularly in urban centers such as West Jakarta. This study aims to design a predictive model of customer satisfaction for RumahNet’s Fiber to the Home (FTTH) services by applying the Decision Tree (C4.5) algorithm. A survey of 250 active subscribers was conducted using a Likert-scale questionnaire distributed through Google Forms, capturing perceptions of internet speed, connection stability, pricing, and technical support. The dataset was processed and analyzed using RapidMiner Studio within the Knowledge Discovery in Databases (KDD) framework. Results show that the model achieved an accuracy of 85.33%, precision of 91.93%, recall of 90.47%, and an F1-score of 91.18%. The decision tree revealed that internet speed and connection stability were the most critical determinants of satisfaction, followed by pricing and responsiveness of customer service. These findings suggest that prioritizing technical reliability while maintaining affordability and responsive support is essential for strengthening loyalty and reducing churn. The research demonstrates that Decision Tree modeling not only provides high predictive accuracy but also offers clear interpretability, making it a valuable tool for data-driven decision-making in the ISP sector.