KLASIFIKASI DATA MENGGUNAKAN JARINGAN SYARAF TIRUAN MODEL FRNN (FULLY RECURRENT NEURAL NETWORK)

Abstract

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). 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 showedt 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 0.1and momentum 0.95, with 30 the real data test, the system accuracy reaches 83.33%.