Analysis and Prediction of Vertical Office Network Bandwidth Using a Backpropagation-Based Neural Network


Muhammad Jamil Asshiddiq(1*); Iffatul Mardhiyah(2);

(1) Universitas Gunadarma
(2) Universitas Gunadarma
(*) Corresponding Author

  

Abstract


The digitalization of business and operational processes in vertical offices has transformed work behaviour, creating a critical need for stable internet connectivity to ensure smooth operations. This issue triggered stakeholders and technical teams to evaluate bandwidth usage trends to enable optimal future planning. Backpropagation, a neural network algorithm, can effectively predict complex patterns using historical data. The ability of the Backpropagation algorithm to adapt to time-series data makes it ideal for forecasting network bandwidth. Therefore, this research aims to analyze and predict network bandwidth requirements using a Backpropagation-Based Neural Network algorithm. This study, which utilized data from October 2022 to September 2024, demonstrates that the Neural Network model provides a high prediction accuracy. The Backpropagation algorithm able to predict the increasing trend in bandwidth usage for October 2024 with a prediction accuracy of 89.08% and a Mean Absolute Percentage Error (MAPE) of 10.92%. Model used in this study can be used as a reference parameter for stakeholders and technical teams within organization for future bandwidth allocation


Keywords


Network Bandwidth; Prediction; Neural Network; Backpropagation; Fifth Keyword

  
  

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doi  https://doi.org/10.33096/ilkom.v17i2.2709.170-185 		  

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