Internet Bandwidth Forecasting by Using Fuzzy Time Series in Zainal Abidin General Hospital, Indonesia - Khalid Rianda; Muhd Iqbal; Muslim Amiren; Maulyanda Maulyanda; Afdhaluzzikri Afdhaluzzikri; Intan Syahrini; Siti Rusdiana; Abdul Fikri; Irvanizam Irvanizam (Infolitika Journal of Data Science, 2026)
Data bandwidth capacity is a critical component of internet infrastructure management, directly impacting network efficiency and operational costs. Accurate measurement and forecasting of bandwidth requirements are essential to optimize resource allocation. This study utilizes a Fuzzy Time Series (FTS) approach for bandwidth forecasting, leveraging its ability to capture complex patterns from historical data without requiring the rigid statistical assumptions of classical forecasting methods. A forecasting model was developed and implemented to predict data bandwidth requirements at the Zainal Abidin General Hospital (RSUZA). Utilizing historical data collected from February 1, 2019, to April 29, 2019, the model's performance was evaluated using the Mean Absolute Percentage Error (MAPE). The proposed method achieved a MAPE of 6.45%, demonstrating high accuracy and falling into the "highly accurate" category.