DECISION TREE BASED INTERNET SIGNAL QUALITY ANALYSIS ON TELKOM INFRASTRUCTURE

Yeni Fitri Afidah (Universitas Ngudi Waluyo)
Sri Mujiyono (Universitas Ngudi Waluyo)

Multidiciplinary Output Research for Actual and International Issue (Morfai Journal)
Vol. 6 No. 2 (2026): Multidiciplinary Output Research For Actual and International Issue

This study analyzes internet signal quality on Telkom infrastructure using the Decision Tree algorithm. Utilizing 5,000 data points from Kaggle, the research classifies network quality into three categories: Good, Fair, and Poor based on parameters such as download speed, ping latency, and packet loss. The evaluation results show that the Decision Tree model achieved an accuracy rate of 98%. Parameters such as download speed and ping latency were identified as the most dominant factors in determining signal quality. These findings prove that a machine learning approach is effective in generating easily interpretable decision rules for network service optimization.