1. Repository Universitas Mulawarman
  2. Akuntabel
  3. J - Computer Sciences and Information Technology
Please use this identifier to cite or link to this item: http://repository.unmul.ac.id/handle/123456789/3263
Title: Daily Network Traffic Prediction Based on Backpropagation Neural Network
Authors: Haviluddin, Haviluddin
Alfred, Rayner
Keywords: Network traffic, BPNN, Prediction, MSE
Issue Date: 13-Dec-2014
Publisher: Australian Journal of Basic and Applied Sciences (AJBAS)
Abstract: Background: The analyzing and predicting network traffic usage is a very important issue in the service activities of the university. Objective: This paper presents the development of Backpropagation neural network (BPNN) algorithms for analyzing and predicting daily network traffic. Results: The gradient descent with momentum (traingdm) algorithm, and two-hidden layers (5-10-5-1) can be used as a model to predict the future. Conclusion: The BPNN technique has been able to approach the performance goals, and also has a pretty good MSE value.
URI: http://repository.unmul.ac.id/handle/123456789/3263
ISSN: 1991-8178
Appears in Collections:J - Computer Sciences and Information Technology

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