Indonesian Crude Oil Price (ICP) Prediction Using Support Vector Regression Algorithm

  • Des Suryani Universitas Islam Riau
  • Mutia Fadhila Universitas Islam Riau
Keywords: ICP, prediction, SVR, RMSE, MAPE


Indonesian crude oil prices (ICP) experience fluctuating movements, influenced by several factors and other conditions that make ICP prices difficult to predict. ICP price prediction can be done with the Support Vector Regression (SVR) method. The information utilized originates from the Ministry of Energy and Mineral Resources' official website, specifically focusing on crude oil pricing data for six primary types of crude oil: SLC, Attaka, Duri, Belida, Banyu, and SC. The data applied covers the time period from January-August 2023. The forecast of the ICP relies on the Dates Brent variable and the Alpha factor through the use of Support Vector Regression (SVR). In the case of a linear kernel, the parameters (epsilon) and C (cost) are determined using the Grid Search algorithm. In the Dated-Brent variable, the best parameter value is obtained with the value of C = 100 and e = 1 while for the Alpha variable, the best parameter value for the type of SLC crude oil is C = 0.01 and e = 0.01, SC value C = 10 and e = 1, Banyu value C = 100 and e = 0.1, Banyu value C = 100 and e = 0.1, Belida value C = 0.01 and e = 0.1, Attaka value C = 0.1 and e = 0.01 and Duri value C = 1 and e = 1. The Alpha value of the main crude oil type is the Duri crude oil type with the lowest RMSE value of 0.9651. The MAPE value for SC crude oil type = 19.55% and Duri = 19.46% is in the good category. The R2 value for Banyu crude oil = 0.60610, SC = 0.42717 and Duri = 0.50421 is in the good category and the MAPE value for Dated-Brent of 49.73% is included in the fair category.


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How to Cite
Des Suryani, & Fadhila, M. (2024). Indonesian Crude Oil Price (ICP) Prediction Using Support Vector Regression Algorithm. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(1), 127 - 135.
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