Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm

Abstract

Crude oil prices play a significant role in the global economy, therefore accurate prediction of oil prices is very important. Therefore, a forecasting model is needed to predict Crude Oil Prices. The purpose of this study is to forecast the price of crude oil from Indonesia (ICP). The data source is from a website published by the Ministry of Energy and Mineral Resources (ESDM), namely monthly crude oil price data specifically for six main types of crude oil: SLC, Attaka, Duri, Belida, Banyu and SC. The data used is data for a period of 5 years (2018 – 2022). The data available is in the form of time series data. Dated Brent combined with the Alpha factor for each month and year is a reference in determining the ICP price. Forecasting Indonesian crude oil prices in the future is based on the historical oil price of the previous period. The Data Mining algorithm used for forecasting is Multiple Linear Regression. The dataset processed using training data is 80%, and testing data is 20%. The model produced, on average, has a good level of accuracy in calculating MAPE where for SLC = 9%, Attaka = 45%, Duri = 126%, Belida = 33%, Banyu = 150% and SC = 50%. Based on the MAPE calculation value, the Linear Regression Equation to predict Indonesian Crude Oil Prices (ICP) shows that the model produced by SLC crude oil is very good. Attaka, Belida and SC crude oil yielded fair yields and Duri and Banyu crude oil yielded poor yields.

Keywords

• crude oil, icp, time series, prediction, linear regression