Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate
DOI:
https://doi.org/10.37385/jaets.v6i2.6117Keywords:
Prediction, MLP, MAPE, Detection RateAbstract
An artificial neural network (ANN) is a network of a group of units to be processed which is modeled based on the behavior of human neural networks. ANN has one of its tasks, namely prediction. Multilayer perceptron (MLP) is one of the ANN methods that can be prediction all of data. Where the prediction needs to be reviewed because the prediction process does not always run normally. So, it takes a good measurement accuracy in order to get an accuracy sensitivity. The accuracy technique in this paper is carried out using Mean Absolute Percentage Error (MAPE) based on absolute error and detection rate. The results obtained with absolute error achieve an accuracy of 99.73% while the accuracy based on the detection rate achieves an accuracy of 99.49%. this can be seen in the case of the prediction of (Indonesian Rupiah) IDR exchange rate against United State Dollar (USD) with the MLP algorithm by testing using MAPE to achieve sensitivity with absolute error.
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