Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 10 Issue 6
Design Passive Optical Network Using Multiclass Classification Neural Network
Firman Pratama Dewantara, Sholeh Hadi Pramono, Rahmadwati
10.7753/IJCATR1006.1005
keywords : PON; QGIS; multiclass-classification; quality-of-service
To require the order of last mile and the increasing demands of high quality of internet, the network architecture of FTTH (Fiber to the Home) has been chosen by numerous ISP (Internet Service Provider). Poor planning does not only increase the infrastructure costs, but it also increases maintenance costs. In this study, the authors focus on the design of passive optical network by using multiclass classification [9] in backpropagation neural network to shorten the FTTH network planning based on passive optical network design to determine final splitter type which refers to the feasibility of QoS (Quality of Service) and Cost Efficiency. Dataset in this study utilizes GIS (Geographic Information System) report with 33 sub-districts in Malang Regency in 2019 data layer. As a result, after 7300 epoch, the accuracy training was 99.99% and the splitter classification accuracy was 98.76%.
@artical{f1062021ijcatr10061005,
Title = "Design Passive Optical Network Using Multiclass Classification Neural Network",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "10",
Issue ="6",
Pages ="156 - 160",
Year = "2021",
Authors ="Firman Pratama Dewantara, Sholeh Hadi Pramono, Rahmadwati"}
The paper proposes to optimize quality-of-service and cost effieciency in design passive optical network.
GIS data is required to collect potential node, length, etc for neural network dataset.
The implementation of Multiclass-Classification in this paper provide the probability of final splitter ratio
- The neural network model has been tuned to obtain high performance val accuracy