Feature Extraction Implementation in the Forecasting Method to Predict Indonesian Oil and Gas Exports and Imports

Authors

DOI:

https://doi.org/10.15294/edukom.v11i1.7879

Keywords:

CatBoost, Data Mining, Exponential Smoothing Forecasting, SARIMAX, XGBoost

Abstract

Future export and import predictions can use data mining and forecasting applications of data mining. Then, normalisation is carried out using datasets taken at the centre of the statistical agency using a mix-max scaler. The normalisation results are then calculated using several forecasting methods, such as Exponential Smoothing, SARIMAX, XGBoost, and CatBoost. The accuracy of this method can be improved by using feature extraction decomposition. They are decomposing, such as trend, residue, and seasonal. The results of the decomposition then become new features that are entered into the prediction model. The prediction results are evaluated using the root mean square error (RMSE). The smaller the RMSE, the better the results. The prediction results without using the method obtained by the Exponential Smoothing method have the best level of accuracy with an average RMSE value of 0.111 and the SARIMAX method with an average RMSE value of 0.146. Meanwhile, the prediction results using the CatBoost and XGBoost feature extraction methods have the best level of accuracy with an RMSE value of 0.046. From the results of the comparison of predictions, the addition of decomposition features to most forecasting methods can significantly increase the accuracy of the calculation.

Author Biographies

  • Michael Anggun Kado Pradana, Mercu Buana University of Yogyakarta
    Michael Anggun Kado Pradana is a dedicated student in the Information Systems  at Universitas Mercu Buana of Yogyakarta, Faculty of Information Technology, specializing in Data Mining since 2020. His academic interests include exploring the latest advancements in data mining and applying innovative solutions to real-world problems. Michael is committed to expanding his knowledge and contributing to the field through both academic and practical experiences.
  • Irfan Pratama, Mercu Buana university Of Yogyakarta

    Irfan Pratama is currently a lecturer in the Information Systems Program at Universitas Mercu Buana Yogyakarta. He earned his Bachelor of Computer Science (S.Kom.) from Universitas Islam Indonesia and his Master of Engineering (M.Eng.) from Universitas Gadjah Mada. Additionally, Irfan holds a Master of Computer Engineering (MCE) and a Master of Cybersecurity Forensics (MCF), demonstrating his commitment to advancing his professional skills and knowledge.

    With a robust academic background and extensive experience in engineering and computer science, Irfan Pratama has made substantial contributions to various projects and initiatives. His research interests encompass Soft Computing, Data Mining, and Machine Learning, areas in which he has been actively involved in both academic exploration and practical application. His dedication to excellence and innovation has established him as a respected figure in the field of information systems.

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Article ID

7879

Published

2024-08-30

Issue

Vol. 11 No. 1 (2024): Edu Komputika Journal

Section

Articles

How to Cite

Pradana, M. A. K., & Pratama, I. (2024). Feature Extraction Implementation in the Forecasting Method to Predict Indonesian Oil and Gas Exports and Imports. Edu Komputika Journal, 11(1), 11-22. https://doi.org/10.15294/edukom.v11i1.7879