Forecasting Gold Prices in Indonesia using Support Vector Regression with the Grid Search Algorithm

Syahfitrri, Nindi
Nonong Amalita
Dodi Vionanda
Zamahsary Martha

UNP Journal of Statistics and Data Science
Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science

Investment is an effort to increase economic growth in Indonesia.  A popular investment in the community is gold investment.  The value of gold investments tends to increase but is not immune from price fluctuations, therefore it is important to forecast the price of gold in Indonesia. The method that can be used to make this forecast is Support Vector Regression (SVR).  SVR is a method that looks for a function that has a deviation of no more than ε to get the target value from all training data. The best SVR model with a linear kernel was obtained from a combination of parameters C=0,0625 and ε=0,001 with a RMSE value of 0,19734 and a value of 0,974112.  So, the SVR method is appropriate to use for forecasting gold prices in Indonesia.