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@article{J.Gauss26727, author = {Silvia Rinjani and Abdul Hoyyi and Suparti Suparti}, title = {PEMODELAN FUNGSI TRANSFER DAN BACKPROPAGATION NEURAL NETWORK UNTUK PERAMALAN HARGA EMAS (Studi Kasus Harga Emas Bulan Juli 2007 sampai Februari 2019)}, journal = {Jurnal Gaussian}, volume = {8}, number = {4}, year = {2019}, keywords = {Gold Price, Crude Oil Prices, Transfer Function,Backpropagation Neural Network, Forecasting}, abstract = { The prestige of investment is increasingly rising as the people educates in managing finances. Gold is an alternative that most people tend to choose to invest. One of the important knowledge in gold investing is to predict the price in the future with factors that influence the price of gold. Therefore, in this research we made a model of gold prices based on crude oil prices. One method to forecast gold prices based on crude oil prices is the transfer function and backpropagation neural network. The results of transfer function model will be used as input for the backpropagation neural network method. The purpose of this research is to get the right forecasting method through the transfer function and backpropagation neural network model that can be used to predict gold prices. The results showed that the transfer function model with b = 0, r = [2], s = 0 and the ARMA noise model (0, [6]) is the best model to forecast the price of gold with the MAPE value of data out sample as 3,3507%. Keywords : Gold Price, Crude Oil Prices, Transfer Function,Backpropagation Neural Network, Forecasting }, issn = {2339-2541}, pages = {474--485} doi = {10.14710/j.gauss.8.4.474-485}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/26727} }
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