Optimasi Parameter Support Vector Machine Untuk Prediksi Nilai Tukar Rupiah Terhadap Dollar Amerika Serikat
With an average daily turnover of $ 5067 billion in April 2016 and $ 5400 in April 2013, it has shown that foreign exchange market is the biggest market and most active of all finacial markets which always move and never static. Therefore the prediction of volatility market is very important to secure investments, manage risks, and decide policies. This research aims to predict the value of exchange rate by using parameters optimization on Support Vector Machines(SVM) Algorithm is applied on it. SVM has been used widely for financial forecasting with time series data set and showed better performance than other algorithms in time series prediction.