Daily Network Traffic Prediction Based on Backpropagation Neural Network

Please use this identifier to cite or link to this item: http://repository.unmul.ac.id/handle/123456789/3263

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DC Field	Value	Language
dc.contributor.author	Haviluddin, Haviluddin	-
dc.contributor.author	Alfred, Rayner	-
dc.date.accessioned	2019-12-13T18:07:15Z	-
dc.date.available	2019-12-13T18:07:15Z	-
dc.date.issued	2014-12-13	-
dc.identifier.issn	1991-8178	-
dc.identifier.uri	http://repository.unmul.ac.id/handle/123456789/3263	-
dc.description.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.	en_US
dc.language.iso	en	en_US
dc.publisher	Australian Journal of Basic and Applied Sciences (AJBAS)	en_US
dc.subject	Network traffic, BPNN, Prediction, MSE	en_US
dc.title	Daily Network Traffic Prediction Based on Backpropagation Neural Network	en_US
dc.type	Article	en_US
Appears in Collections:	J - Computer Sciences and Information Technology

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