Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression

Authors

  • Mufni Alida Gunadarma University, Indonesia
  • Metty Mustikasari Gunadarma University, Indonesia

DOI:

https://doi.org/10.15575/join.v5i1.537

Keywords:

Linear, Polynomial, Radial Basis Function, Regression, Support Vector Regression

Abstract

As a developing country, Indonesia is affected by fluctuations in foreign exchange rates, especially the US Dollar. Determination of foreign exchange rates must be profitable so a country can run its economy well. The prediction of the exchange rate is done to find out the large exchange rates that occur in the future and the government can take the right policy. Prediction is done by one of the Machine Learning methods, namely the Support Vector Regression (SVR) algorithm. The prediction model is made using three kernels in SVR. Each kernel has the best model and, the accuracy and error values are compared. The Linear Kernel has C = 7, max_iter = 100. The Polynomial Kernel has gamma = 1, degree = 1, max_iter = 4000 and C = 700. The RBF kernel has gamma = 0.03, epsilon = 0.007, max_iter = 2000 and C = 100. Linear kernels have advantages in terms of processing time compared to Polynomial and Radial Basis Function (RBF) kernels with an average processing time of 0.18 seconds. Besides that, in terms of accuracy and error, the RBF kernel has advantages over the Linear and Polynomial kernels with the value R2 = 95.94% and RMSE = 1.25%.

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Published

2020-07-16

Issue

Vol. 5 No. 1 (2020)

Section

Article

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