DOI: https://doi.org/10.14710/jati.17.3.219-228

DESIGN OF EXPERIMENT PADA ARTIFICIAL NEURAL NETWORK UNTUK MEMPREDIKSI KURS TUKAR MATA UANG IDR/USD

*Rizka Britania orcid  -  Universitas Bina Nusantara, Indonesia

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Abstract

Prediksi kurs tukar mata uang memiliki peranan penting dalam bisnis, salah satunya dalam hal international purchasing. Terdapat beberapa metode yang digunakan dalam melakukan forecasting mata uang, salah satunya adalah Artificial Neural Network (ANN). Berdasarkan beberapa penelitian terdahulu, ANN terbukti superior dibandingkan metode forecasting lainnya dalam memprediksi kurs tukar mata uang. Salah satu kelemahan ANN adalah tidak adanya setting parameter yang baku untuk digunakan, sehingga setting parameter yang berbeda dapat memberikan hasil akurasi yang berbeda. Penelitian ini bertujuan menentukan dua nilai parameter yang memiliki pengaruh signifikan, yaitu jumlah input node dan jumlah hidden node melalui metode design of experiment dalam memprediksi kurs tukar mata uang IDR/USD. Bobot awal dan bias pada replikasi yang memberikan performansi lebih baik dari replikasi sebelumnya disimpan untuk selanjutnya digunakan dalam membangkitkan forecast pada periode selanjutnya. Hasil penelitian menunjukkan bahwa delapan input nodes dan empat hidden nodes memberikan akurasi terbaik yang ditandai dengan nilai MSE test terendah. Selain itu, berdasarkan grafik perilaku MSE test dari setiap arsitektur jaringan yang terbentuk, dapat disimpulkan bahwa dalam memprediksi kurs tukar mata uang IDR/USD, jumlah hidden nodes bersifat lebih sensitif dibanding jumlah input nodes.

 

Abstract

Design of Experiment in Artificial Neural Network to Forecast Foreign Exchange Rate IDR/USD]. Forecasting foreign exchange rate plays a significant role in business, for example in international purchasing. There are several methods used in forecasting foreign exchange rates, one of them is the Artificial Neural Network (ANN). Based on several earlier literatures, ANN has been proven as a superior method in forecasting foreign exchange rate compared to other methods. However, ANN has several weaknesses, for example, there is no standard parameters setting used in ANN, thus different parameters setting could lead to different accuracy. This research aims to determine two crucial parameters that give significant impact to the ANN model built; the number of input nodes and the number of hidden nodes, through the design of an experiment to forecast the IDR/USD exchange rate. Initial weights and bias in replication that give better performance than earlier replication are stored and used to forecast the data for next periods as needed. The result of this research shows that eight input nodes and four hidden nodes give the best accuracy to forecast IDR/USD exchange rate which is proven by the lowest MSE test score. Moreover, based on the MSE test behavior graph, the number of hidden nodes is more sensitive than the number of input nodes in forecasting IDR/USD exchange rate.

Keywords: Artificial Neural Network; design of experiment; forecast; foreign exchange rate

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Keywords: Artificial Neural Network; design of experiment; forecast; kurs tukar mata uang

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  1. Chen, J. C., & Narala, N. H. R. (2017). Forecasting currency exchange rates via Feedforward Backpropagation Neural Network. Universal Journal of Mechanical Engineering, 5(3), 77–86. https://doi.org/10.13189/ujme.2017.050302
  2. Dunis, C. L., Laws, J., & Schilling, U. (2012). Currency trading in volatile markets: Did neural networks outperform for the EUR/USD during the financial crisis 2007-2009. Journal of Derivatives and Hedge Funds, 18(1), 2–41. https://doi.org/10.1057/jdhf.2011.31
  3. Erdogan, O., & Goksu, A. (2014). Forecasting Euro and Turkish Lira Exchange Rates with Artificial Neural Networks (ANN). International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(4), 307–316. https://doi.org/10.6007/ijarafms/v4-i4/1361
  4. Galeshchuk, S. (2016). Neural networks performance in exchange rate prediction. Neurocomputing, 172, 446–452. https://doi.org/10.1016/j.neucom.2015.03.100
  5. Jena, P. R., Majhi, R., & Majhi, B. (2015). Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate prediction. Journal of King Saud University - Computer and Information Sciences, 27(4), 450–457. https://doi.org/10.1016/j.jksuci.2015.01.002
  6. Kumar Chandar, S., Sumathi, M., & Sivanandam, S. N. (2015). Forecasting of foreign currency exchange rate using neural network. International Journal of Engineering and Technology, 7(1), 99–108
  7. Kumar, V., & Tripathi, M. M. (2019). Weight and bias initialization of ANN for load forecasting using cuckoo search algorithm. Proceedings - 2019 7th International Conference on Green and Human Information Technology, ICGHIT 2019, January, 60–65. https://doi.org/10.1109/ICGHIT.2019.00021
  8. Lachtermacher, G., & Fuller, J. D. (1995). Backpropagation in Time-series Forecasting. 14, 381–393
  9. Lavanya, V., & Parveentaj, M. (2013). Foreign Currency Exchange Rate (FOREX) using Neural Network. International Journal of Science and Research, 2(10), 174–177. https://www.academia.edu/5320333/Foreign_Currency_Exchange_Rate_FOREX_using_Neural_Network
  10. Lee, A., Geem, Z. W., & Suh, K. D. (2016). Determination of optimal initial weights of an artificial neural network by Using the harmony search algorithm: Application to breakwater armor stones. Applied Sciences (Switzerland), 6(6). https://doi.org/10.3390/app6060164
  11. Meng, L., & Sun, Y. (2013). Research on automated forex trading system based on BP neural network. Advanced Materials Research, 753–755, 3080–3083. https://doi.org/10.4028/www.scientific.net/AMR.753-755.3080
  12. Nag, A. K., & Mitra, A. (2002). Forecasting daily foreign exchange rates using genetically optimized neural networks. Journal of Forecasting, 21(7), 501–511. https://doi.org/10.1002/for.838
  13. Nizar, M. A. (2012). Pengaruh Jumlah Turis Dan Devisa Pariwisata Terhadap Nilai Tukar Rupiah. Jurnal Kepariwisataan Indonesia, 7(2). https://ideas.repec.org/p/pra/mprapa/65629.html
  14. Panda, C., & Narasimhan, V. (2007). Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling, 29(2), 227–236. https://doi.org/10.1016/j.jpolmod.2006.01.005
  15. Rout, A. (2013). Efficient Forecasting of Exchange rates with Recurrent FLANN. IOSR Journal of Computer Engineering, 13(6), 21–28. https://doi.org/10.9790/0661-1362128
  16. Thakur, G. S. M., Bhattacharyya, R., & Mondal, S. S. (2016). Artificial Neural Network Based Model for Forecasting of Inflation in India. Fuzzy Information and Engineering, 8(1), 87–100. https://doi.org/10.1016/j.fiae.2016.03.005
  17. Thorbecke, W., & Kato, A. (2012). The effect of exchange rate changes on Japanese consumption exports. Japan and the World Economy, 24(1), 64–71. https://doi.org/10.1016/j.japwor.2011.12.004
  18. Tung, L. T. (2019). Does exchange rate affect the foreign tourist arrivals? Evidence in an emerging tourist market. Management Science Letters, 9(8), 1141–1152. https://doi.org/10.5267/j.msl.2019.5.001
  19. Ye, S. (2012). RMB Exchange Rate Forecast Approach Based on BP Neural Network. Physics Procedia, 33. https://doi.org/10.1016/j.phpro.2012.05.064
  20. Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. In International Journal of Forecasting (Vol. 14, Issue 1). https://doi.org/10.1016/S0169-2070(97)00044-7
  21. Zhang, G., & Hu, M. Y. (1998). Neural network forecasting of the British pound/US dollar exchange rate. Omega, 26(4), 495–506. https://doi.org/10.1016/S0305-0483(98)00003-6
  22. Zhao, Y. (2020). The influence and impact of the exchange rate on the economy. E3S Web of Conferences, 214. https://doi.org/10.1051/e3sconf/202021403007
  23. Peraturan BI mengenai Sistem Pembayaran, diperoleh melalui situs internet:
  24. http://www.bi.go.id/id/peraturan/sistem-pembayaran/Pages/pbi_170315.aspx
  25. Diunduh pada 27 Mei 2017

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