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

  • Des Suryani Universitas Islam Riau
  • Mutia Fadhilla University of Islam Riau
  • Ause Labellapansa University of Islam Riau
Keywords: crude oil, ICP, time series, prediction, linear regression

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.

Downloads

Download data is not yet available.

References

G. Khuziakhmetova, V. Martynov, and K. Heinrich, “DSS for Oil Price Prediction Using Machine Learning,” vol. 166, no. Itids, pp. 89–94, 2019.

Y. N. Kunang et al., “Analysis and implementation of the Port Knocking method using Firewall-based Mikrotik RouterOS,” IOP Conf. Ser. Mater. Sci. Eng., vol. 8, no. 4, pp. 1907–5022, 2019.

Y. Chen, Y. Zou, Y. Zhou, and C. Zhang, “Multi-step-ahead Crude Oil Price Forecasting based on Grey Wave Forecasting Method,” Procedia Comput. Sci., vol. 91, pp. 1050–1056, 2016.

H. Rong, A. P. Teixeira, and C. Guedes Soares, “Data mining approach to shipping route characterization and anomaly detection based on AIS data,” Ocean Eng., vol. 198, p. 106936, 2020.

A. Veno, L. A. Safitri, and T. Prijanto, “Analisis Daya Saing Ekspor Minyak Mentah Indonesia Dibanding dengan Negara Anggota OPEC,” Triangle 1, vol. 1, no. 1, pp. 16–29, 2020.

C. EL AMRANI and H. GIBET TANI, “Smarter round robin scheduling algorithm for cloud computing and big data,” J. Data Min. Digit. Humanit., 2018.

S. Gao and Y. Lei, “A new approach for crude oil price prediction based on stream learning,” Geosci. Front., vol. 8, no. 1, pp. 183–187, 2017.

A. Sepp, “Machine Learning for Volatility Trading (Presentation Slides),” SSRN Electron. J., no. May, 2018.

I. H. Witten, “Data Mining Practical Machine Learning Tools and Techniques (Fourth Edition),” Morgan Kaufmann. pp. 417–466, 2017.

M. Hussein and Y. Azhar, “Prediksi Harga Minyak Dunia Dengan Metode Deep Learning,” Fountain Informatics J., vol. 6, no. 1, pp. 26–34, 2021.

D. Suryani, A. Yulianti, E. L. Maghfiroh, and J. Alber, “Quality Classification of Palm Oil Products Using Naïve Bayes Method,” Sistemasi, vol. 11, no. 1, p. 251, 2022.

A. Fitri Boy, “Implementasi Data Mining Dalam Memprediksi Harga Crude Palm Oil (CPO) Pasar Domestik Menggunakan Algoritma Regresi Linier Berganda (Studi Kasus Dinas Perkebunan Provinsi Sumatera Utara),” J. Sci. Soc. Res., vol. 4307, no. 2, pp. 78–85, 2020.

Suyanto, Machine Learning - Tingkat Dasar dan Lanjut, Pertama. Bandung: Informatika, 2018.

S. Wang, J. Cao, and P. Yu, “Deep learning for spatio-temporal data mining: A survey,” IEEE Trans. Knowl. Data Eng., 2020.

G. N. Ayuni and D. Fitrianah, “Penerapan metode Regresi Linear untuk prediksi penjualan properti pada PT XYZ,” J. Telemat., vol. 14, no. 2, pp. 79–86, 2019.

A. Izzah and R. Widyastuti, “Prediksi Harga Saham Menggunakan Improved Multiple Linear Regression untuk Pencegahan Data Outlier,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 2, no. 3, pp. 141–150, 2017.

A. K. Marandi and D. A. Khan, “An Impact of Linear Regression Models for Improving the Software Quality with Estimated Cost,” Procedia Comput. Sci., vol. 54, no. April 2016, pp. 335–342, 2015.

N. Tomasevic, N. Gvozdenovic, and S. Vranes, “An overview and comparison of supervised data mining techniques for student exam performance prediction,” Comput. Educ., vol. 143, p. 103676, 2020.

Y. Supriyanto, “Prediksi Harga Minyak Kelapa Sawit Menggunakan Linear Regression Dan Random Forest,” J. Ilm. Wahana Pendidik., vol. 8, no. 7, pp. 178–185, 2022.

N. Aghdaei, G. Kokogiannakis, D. Daly, and T. McCarthy, “Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings,” Energy Procedia, vol. 121, pp. 79–86, 2017.

Published
2022-12-30
How to Cite
Des Suryani, Mutia Fadhilla, & Ause Labellapansa. (2022). Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(6), 1057 - 1063. https://doi.org/10.29207/resti.v6i6.4590
Section
Artikel Teknologi Informasi