Indonesian Crude Oil Price (ICP) Prediction Using Support Vector Regression Algorithm
Abstract
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 frame from January 2018 to August 2023. The forecast of the ICP relies on the date 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 = 1 while for the Alpha variable, the best parameter value for the SLC crude oil type is C= 0.01 and = 0.01, SC value C = 10 and = 1, Banyu value C = 100 and = 0.1, Banyu value C = 100 and = 0.1, Belida value C = 0.01 and = 0.1, Attaka value C = 0.1 and = 0.01 and Duri value C = 1 and = 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.
Downloads
References
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.
F. Agung (2021), “Menteri ESDM ubah formula harga minyak mentah Indonesia. Online at https://industri.kontan.co.id/news/menteri-esdm-ubah-formula-harga-minyak-mentah-indonesia, accessed 7 November 2023.”
Y. Chen, Y. Zou, Y. Zhou, and C. Zhang, “Multi-step-ahead Crude Oil Price Forecasting based on Grey Wave Forecasting Method,” Procedia - Procedia Comput. Sci., vol. 91, pp. 1050–1056, 2016.
G. Khuziakhmetova, V. Martynov, and K. Heinrich, “DSS for Oil Price Prediction Using Machine Learning,” vol. 166, no. Itids, pp. 89–94, 2019.
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. Suryani, Des; Fadhila, Mutia; Labellapansa, “Indonesian Crude Oil Price ( ICP ) Prediction Using Multiple Linear,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 158, pp. 8–12, 2022.
A. Bode, “Perbandingan Metode Prediksi Support Vector Machinedan Linier Regression Menggunakan Backward Elimination Pada Produksi Minyak Kelapa,” Simtek J. Sist. Inf. dan Tek. Komput., vol. 4, no. 2, pp. 104–107, Oct. 2019.
J. Veri, S. Surmayanti, and G. Guslendra, “Prediksi Harga Minyak Mentah Menggunakan Jaringan Syaraf Tiruan,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 3, pp. 503–512, 2022.
P. Jiang, Z. Liu, X. Niu, and L. Zhang, “A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting,” Energy, vol. 217, p. 119361, 2021.
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.
G. Hu, Z. Xu, G. Wang, B. Zeng, Y. Liu, and Y. Lei, “Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression,” Energy, vol. 224, p. 120153, 2021.
H. Guo, H. Nguyen, X. N. Bui, and D. J. Armaghani, “A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET,” Eng. Comput., vol. 37, no. 1, pp. 421–435, 2021.
R. A. Putri, W. S. Winahju, and M. Mashuri, “Penerapan Metode Ridge Regression dan Support Vector Regression (SVR) untuk Prediksi Indeks Batubara di PT XYZ,” J. Sain dan Seni ITS, vol. 9, no. 1, pp. 64–71, 2020.
D. Parbat and M. Chakraborty, “A python-based support vector regression model for prediction of COVID19 cases in India,” Chaos, Solitons and Fractals, vol. 138, pp. 3–7, 2020.
D. E. Krislianti, E. Zukhronah, and Y. Susanti, “Peramalan Harga Minyak Menggunakan Autoregressive Integrated Moving Average dan Support Vector Regression,” Pros. Semin. Pendidik. Mat. dan Mat., vol. 7, no. 2721, pp. 1–8, 2023.
R. E. Caraka, H. Yasin, and A. W. Basyiruddin, “Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis,” J. Mat., vol. 7, no. 1, p. 43, 2017.
M. Naqi, “A Macroeconomic Model for Forecasting Crude Oil Prices with Feedforward Neural Network Grid Search Experimentation,” 2019.
D. D. Monteiro, M. M. H. Duque, G. S. Chaves, V. M. Ferreira Filho, and J. S. Baioco, “Using data analytics to quantify the impact of production test uncertainty on oil flow rate forecast,” Oil Gas Sci. Technol., vol. 75, 2020.
K. ESDM, Pedoman Pelaksanaan Penyusunan, Evaluasi, Persetujuan Rencana Kerja dan Anggaran Biaya, serta Laporan Pada Kegiatan Usaha Pertambangan Mineral dan Batubara. Jakarta, 2018.
S. A. Nitami and B. Hayati, “Relationship Between Crude Oil Price Fluctuations, Economic Growth, Inflation, and Exchange Rate in Indonesia 1967-2019,” AFEBI Econ. Financ. Rev., vol. 6, no. 2, p. 83, 2021.
E. M. de Oliveira and F. L. Cyrino Oliveira, “Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods,” Energy, vol. 144, pp. 776–788, 2018.
C. Tofallis, “A better measure of relative prediction accuracy for model selection and model estimation,” J. Oper. Res. Soc., vol. 66, no. 8, pp. 1352–1362, 2015.
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.
S. Kim and H. Kim, “A new metric of absolute percentage error for intermittent demand forecasts,” Int. J. Forecast., vol. 32, no. 3, pp. 669–679, 2016.
K. Gajowniczek and T. Zabkowski, “Two-stage electricity demand modeling using machine learning algorithms,” Energies, vol. 10, no. 10, 2017.
N. Vasilyeva, E. Fedorova, and A. Kolesnikov, “Big data as a tool for building a predictive model of mill roll wear,” Symmetry (Basel)., vol. 13, no. 5, 2021.
J. A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression,” Stat. Comput., vol. 18, no. 1, pp. 199–222, 2004.
Z. Wang, H. Xu, L. Xia, Z. Zou, and C. G. Soares, “Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion,” Ocean Eng., vol. 216, no. May, p. 107994, 2020.
Copyright (c) 2024 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
