Quickpropagation Architecture Optimization Based on Input Pattern for Exchange Rate Prediction from Rupiah to US Dollar

Harits Farras Zulkarnaen, Sukmawati Nur Endah

Abstract


Money exchange between countries was done by using exchange rates. One of the examples was the exchange between Rupiah and US Dollar. Exchange rates prediction to US Dollar was an attempt to assist all related economic actors to avoid losses during the process of decision making. The prediction could be done by using artificial neural network method. Quickpropagation was one of artificial neural network models considered suitable for prediction. Quickpropagation network architecture consisted of input layer, hidden layer, and output layer. The input layer of quickpropagation architecture could be determined by using autoregression (AR) for the input pattern. In this research, the authors aim to optimize the quickpropagation network architecture method using Nguyen-Widrow weight initialization to predict the Rupiah exchange rate to US Dollar. The research data were the exchange rate from the BI website from May 2017 to July 2017 with a total of 57 data. The test was performed by using K-Fold Cross Validation with k = 11 values for data without AR and k = 8 for AR data. The results show that quickpropagation method using AR has better performance than quickpropagation method without AR in terms of MSE training and testing. The best parameters are in alpha 0,6 and hidden neuron 5, with MSE training value 0,03272 and MSE testing 0,02873 for selling rate and at alpha 0,9 and hidden neuron 5, with MSE training value 0,03297 and MSE testing 0,02828 for buying rate with maximal epoch 100.000 and target error 0,05.


Keywords


Predicted Rupiah exchange rate, selling rate, buying rate, Quickpropagation, Autoregression (AR)

Full Text:

PDF

References


Fahlman, S. E., 1988. An Empirical Study of Learning Speed in Back-Propagation Networks, United States: Carneige Mellon University.Gurney, K., 1997. An Introduction to Neural Networks. London: UCL Press Limited.

Filik, U. B. & Kurban, M., 2007. A New Approach for the Short-Term Load Forecasting with Autoregressive and Artificial Neural Network Models. International Journal of Computational Intelligence Research, Volume 3, pp. 66-71.

Gurney, K., 2003. An Introduction to Neural Networks. London: UCL Press.

Harinowo, Cyrillus., 2007. Manajemen Aktiva Pasiva Bank Devisa. jakarta: Grasindo.

Hutauruk, A. B., Jondri & Rismala, R., 2010. Perancangan Model Sistem Prediksi Nilai Tukar Rupiah terhadap Dollar Amerika.

Kohavi, R., 1995. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Stanford, Stanford University, p. 2.

Mirzadeh, H. & Najafizadeh, A., 2008. Correlation between processing parameters and strain-induced martensitic transformation in cold worked AISI 301 stainless steel. Material's characterization, Volume 59, pp. 1650-1654.Siang, J. J., 2005. Jaringan Syaraf Tiruan & Pemrogramannya Menggunakan Matlab. Yogyakarta: ANDI

Samarasinghe, S., 2006. Neural Networks for Applied Sciences and Engineering. New York: Auerbach Publications.

Wei, W., 1990, Time Series Analysis. Canada : Addison-Wesley Publishing Company




DOI: https://doi.org/10.15294/sji.v5i2.15889

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.