| dc.description.abstract | The growth of internet usage in rural areas is accelerating along with the development of information technology. However, internet infrastructure in these areas is often limited, posing challenges in bandwidth management to provide optimal services to users. Ineffective bandwidth scheduling can cause delays, loss, and overflow of bandwidth on the network. Statistical Testing Scheduling methods such as Autoregression, Auto Regressive Moving Average (ARMA), Auto Regressive Integrated Moving Average (ARIMA), FIFO, and Round Robin are tested to evaluate their performance in bandwidth management. Visualization of Statistical Analysis Results The statistical analysis results are visualized to provide a better understanding of the performance comparison between the scheduling methods. The research shows the effectiveness of the FIFO method in bandwidth scheduling. Variables such as buffer capacity and traffic type affect scheduling performance. Optimization of the method and adjustment to network load and applications are recommended. Extending the research with additional variables and experimental validation can improve the understanding and applicability of the findings. Overall, the conclusion of this research is that the selection of bandwidth allocation prediction methods and scheduling methods is critical in optimizing network performance. The optimal method may scale depending on the specific conditions of the network, such as bandwidth, buffers, and other performance parameters. These findings can be the basis for the development of more effective scheduling strategies in network management | en_US |
