| 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 |
