| Summary: | Bandwidth management for UGM - Hotspot Wireless network in the
Department of Electrical Engineering and Information Technology UGM is still
managed manually by looking at traffic data at one time then share it manually. It
make bandwidth sharing becomes less effective. It is necessary to automatically
adjust bandwidth allocation based on the everyday use. However, before reaching
the automation process, it requires an analysis to find the appropriate forecasting
model that can be applied in the development of automation.
Different types of methods are used to produce accurate forecasting of
network bandwidth, one of them is ARIMA (Autoregressive Integrated Moving
Average) method. However, this method is less accurate for modeling the UGM -
Hotspot network traffic data because its seasonal trends. Thus, this study using
Seasonal ARIMA( SARIMA ) with the addition of outlier detection so that the
result becomes more accurate .
Based on the result, MAPE(Mean Absolute Percentage Error) for SARIMA
model with outlier detection (14 %) is better than SARIMA model without outlier
detection (32 %).
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