MODEL PERAMALAN NILAI TUKAR MATA UANG MENGGUNAKAN METODE HYBRID GLARANN

https://doi.org/10.22146/jmt.59773

Yogya Ardi Winata(1), Subanar Subanar(2*)

(1) Universitas Gadjah Mada
(2) Departemen Matematika Fakultas MIPA, Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Metode hybrid GLARANN merupakan gabungan dari model Generalized Lin-
ear Autoregression (GLAR) yang termasuk model linear dan Artificial Neural Network
(ANN) yang digolongan dalam model nonlinear. Gagasan metode hybrid GLARANN
adalah menggunakan kelebihan dari masing-masing model, yaitu model GLAR dalam
mendeteksi pola linear dan ANN dalam mendeteksi pola nonlinear pada data runtun
waktu. Tujuan penelitian ini adalah untuk mengetahui prosedur penyusunan model pera-
malan metode hybrid GLARANN, serta aplikasi dan efektifitasnya. Aplikasi metode hy-
brid GLARANN yaitu pada peramalan nilai tukar rupiah (IDR) terhadap beberapa mata
uang asing, yaitu dollar Amerika (USD), euro (EUR), yen Jepang (JPY), dollar Hongkong
(HKD), dollar Australia (AUD), dan dollar Singapura (SGD), dengan nilai ekspor seba-
gai variabel eksogen. Hasil peramalan menunjukkan metode hybrid GLARANN efektif
dalam peramalan AUD berdasarkan nilai RMSE, MAE, MAPE dan NMSE. Namun tidak
efektif pada peramalan HKD, SGD, dan USD. Pada peramalan EUR dan JPY, model
GLAR merupakan metode yang paling efektif. Sedangkan, metode hybrid GLARANN
hanya lebih efektif daripada ANN berdasarkan RMSE dan NMSE.


Keywords


Metode hybrid; model GLAR; neural network; peramalan; nilai tukar mata uang.

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References

Fausett, L., Fundamentals of Neural Networks Architectures, Algorithms, and Application, Prentice Hall, New Jersey, 1994.

Gujarati, D.N., Basic Econometrics Fourth Edition, The McGraw-Hill Company, New York, 2003.

Haykin, S., Neural Networks and Learning Machines, Pearson prentice Hall, New Jersey, 2009.

L¨utkepohl, H., Introduction to Multiple Time Series Analysis, Springer-Verlag Berlin Heidelberg, New York, 1991.

L¨utkepohl, H., dan Kr¨atzig, M., Applied Time Series Econometrics, Cambridge University Press, New York, 2004.

Yu, L., Wang, S., dan Lai, K. K., Foreign-Exchange-Rate Forecasting With Artificial Neural Networks, Springer Science+Business Media, New York., 2007.

Abdulkadir, S. J., Suet, P. Y., Maran, M., dan Fong, W. L., Hybridization of Ensemble Kalman Filter and Non-linear Autoregressive Neural Network for Financial Forcasting, Springer International Publishing Switzerland, 72-81, 2014.

Carriero, A., Kapetanios, G., dan Marcellino, M., Forecasting Exchange Rates With A Large Bayesian VAR, International Journal of Forecasting, 25, 400-417, 2009.

De Matos, G., Neural Networks for Forecasting Exchange Rate, Thesis, The University of Manitoba, Canada, 1994.

El Shazly, M.R. dan El Shazly, H.E., Forecasting Currency Prices Using a Genetically Evolved Neural Network Architecture, International Review of Financial Analysis, 8(1), 67-82, 1999.

Galeshchuk, S., Neural Networks Performance in Exchange Rate Prediction, Neurocomputing, 172, 446-452, 2016.

Khashei, M. dan Bijari, M., An Artificial Neural Network (p, d, q) Model for Timeseries Forecasting, Expert Systems with Applications, 37, 479-489, 2010.

Khashei, M., Mehdi B., dan Gholam, A. R. A., Hybridization of Autoregressive Integrated Moving Average (ARIMA) with Probabilistic Neural Networks (PNNs), Computers and Industrial Engineering, 63, 37-45, 2012.

Meese, R. dan Rogoff, K., Empirical Exchange Rate Models of The Seventies: Do They Fit Out of Sample?, Journal of International Economics, 14, 3-24, 1983.

Shephard, N., Generalized Linear Autoregressions, Economics Working Paper, 8, Nuffield College, Oxford, 1985.

Suhartono, Feedforward Neural Network untuk Pemodelan Time Series, Disertasi, Universitas Gadjah Mada, 2007.

Wang, J. J., Jian, Z. W., Zhe, G. Z., dan Shu, P. G., Stock Index Forecasting Based on Hybrid Model, Omega, 40, 758-766, 2012.

Wu, B., Model-Free Forecasting for Nonlinear Time Series (with Application to Exchange Rates), Computational Statistics and Data Analysis, 19, 433459, 1995.

Yu, L., Wang, S., dan Lai, K. K., A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR and ANN For Foreign Exchange Rates, Computers and Operations Research, 32, 2523-2541, 2005.

Zhang, G.P., Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model, Neurocomputing, 50, 159-175, 2003.



DOI: https://doi.org/10.22146/jmt.59773

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