Optimasi Parameter Support Vector Regression Dengan Algoritme Genetika Untuk Prediksi Harga Emas - Muthia Azzahra; Budi Darma Setiawan; Putra Pandu Adikara (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Gold is one of the precious metals that many people interested as commodity to invest because of its resistance to inflation. Fluctuations can occur so extreme that affect the value of gold. Therefore, prospect of gold value in the future is quite important for the investors. One of prediction methods is Support Vector Regression (SVR), but the sensitivity of SVR parameters could influence the prediction result, therefore Genetics Algorithm (GA) can be applied, this method is flexible enough to be hybridized. This study discuss about the optimization of SVR parameters using GA to predict gold prices. Based on the testing result, the best mean absolute percentage error (MAPE) is 0.2407% with SVR loop 50, GA's generation 95, population size 70, crossover rate 0.01, mutation rate 0.99, elitism percentange 80%, range of 1x10-7-1x10-4, range of 0.01-5, range of 1x10-7-1x10-4, range of 1x10-5-1x10-4, and range of 1x10-3-0.1.