FORECASTING INTERNET BANDWIDTH USAGE WITH TIME SERIES ARIMA APPROACH USING PARAMETER OPTIMIZATION - Rahmawan Bagus Trianto; Kukuh Muhammad; Joko Purwanto; Adlan Nugroho (Journal of Computer Science and Technology (JCS-TECH), 2025)
Efficient internet bandwidth management is a critical challenge in network administration, especially in institutions with high data usage. Significant fluctuations in internet traffic can cause bottlenecks and a decline in service performance. Therefore, the ability to accurately estimate bandwidth requirements becomes crucial. This research implements the Autoregressive Integrated Moving Average (ARIMA) time series forecasting method to predict bandwidth usage on a local network over a one-month period. The data uses a dummy dataset of 100 rows aimed at analyzing the performance of the ARIMA model and is analyzed using Python with statistical libraries such as Pandas, Statsmodels, and Matplotlib. The ARIMA model was selected based on the results of the stationarity test and ACF/PACF analysis. Performance evaluation was conducted using RMSE. The research results show that the ARIMA(4,2,7) model provides high prediction accuracy with an RMSE value of 3.276. This estimation can assist network administrators in proactively planning capacity, avoiding service disruptions due to overload, and supporting data-driven decision-making in network management.