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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  25. Diunduh pada 27 Mei 2017

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