BibTex Citation Data :
@article{J.Gauss26636, author = {Ekky Wigati and Budi Warsito and Rita Rahmawati}, title = {PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT}, journal = {Jurnal Gaussian}, volume = {7}, number = {1}, year = {2018}, keywords = {artificial neural network; cascade forward; exchange rate; MAPE}, abstract = { Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1 st , 2015 until December 31 st , 2017. The best model was built from 1 unit input layer with input Z t-1 , 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords : artificial neural network, cascade forward, exchange rate, MAPE }, issn = {2339-2541}, pages = {64--72} doi = {10.14710/j.gauss.7.1.64-72}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/26636} }
Refworks Citation Data :
Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%.
Keywords: artificial neural network, cascade forward, exchange rate, MAPE
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