PENENTUAN SATUAN KERJA BAGI BINTARA POLISI BARU PADA SEKOLAH POLISI NEGARA MENGGUNAKAN FULLY RECURRENT NEURAL NETWORK (STUDI KASUS : SPN KUPANG)
Artificial Neural Networks (ANN) can be used to solve specific problems such as prediction, classification, processing data, and robotics. Based on the exposure, this study tried to develop a system by applying ANN models Fully Recurrent Neural Network (FRNN) to deal with the problems of classificat...
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| Main Authors: | , |
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| Format: | Theses and Dissertations NonPeerReviewed |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/129838/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=70241 |
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| Institution: | Universitas Gadjah Mada |
| Summary: | Artificial Neural Networks (ANN) can be used to solve specific problems such as prediction, classification, processing data, and robotics. Based on the exposure, this study tried to develop a system by applying ANN models Fully Recurrent Neural Network (FRNN) to deal with the problems of classification determination unit for New Police Officer at SPN Kupang include DitSabara, DitPolAir, and SatBrimob, which has been using the system manually, the ANN system Fully Recurrent Neural Networks can provide accurate information to the SPN Kupang to determine the right decision.
Fully Recurrent Neural Network structures have been presence of feedback that can make faster iteration thus making the update parameters and convergence speed become faster. The learning method used is Backpropagation Through Time. The system is implemented using the C# program. Input vectors used consisted of 7 variables.
The results showed t the developed system will rapidly converge and able to achieve the most optimal error value (minimum error) when using one hidden layer with 17 units of the number of neurons . The best accuracy can be obtained using the LR of 0.001 , target of 0.1 and momentum 0.95, with 25 test data of data, the system accuracy reaches 96%, while the real data, the accuracy reached 83.33%. |
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