Forecasting Model of Indonesia's Oil & Gas and Non-Oil & Gas Export Value using Var and LSTM Methods

Khaidar Ahsanur Rijal, Anik Vega Vitianingsih, Yudi Kristyawan, Anastasia Lidya Maukar, Seftin Fitri Ana Wati

Abstract


As a country with abundant natural resources in the form of mineral and non-mineral products, Indonesia is characterized by its ability to fulfill domestic and foreign needs through export activities categorized into two commodities: oil and gas and non-oil and gas. Export activities are an indicator of the country's economic growth that often fluctuates in value, and these conditions are fundamentally caused by a decrease in production quantity and the instability of the global economic climate. The strategy to overcome these problems is to create a forecasting model. This research aims to develop a forecasting model using time series analysis methods, including vector autoregressive (VAR) and long short-term memory (LSTM) methods based on oil and non-oil and gas value parameters. The results of the Granger causality test stated that the values of oil and gas and non-oil and gas affect each other. The VAR model with the optimum lag produced by the Akaike Information Criterion (AIC) test obtained an accuracy value of MAPE oil & gas and non-oil and gas of 18.4% and 32.1%, respectively. LSTM generates the best model with a MAPE value of 6,23% for oil & gas and 8,18% for non-oil and gas.

Keywords


Forecasting Model; Export Value of Oil and Gas; Non-Oil and Gas Export; Deep Learning; Machine Learning; VAR method; LSTM Method.

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References


Farina, F., Husaini, A. (2017). Pengaruh Dampak Perkembangan Tingkat Ekspor dan Impor terhadap Nilai Tukar Negara ASEAN Per Dollar Amerika Serikat. J. Adm. Bisnis S1 Univ. Brawijaya. vol. 50, no. 6, pp. 44–50.

Statistik, B. P. (2023). Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS.

Republic, I. (2001). Undang-Undang Republik Indonesia Nomor 22 Tahun 2001 Tentang Minyak Dan Gas Bumi. Biol. vol. 159, no. 7, p. 1.

Kertayuga, D., Santoso, E., Hidayat, N. (2001). Prediksi Nilai Ekspor Impor Migas dan Non-Migas Indonesia Menggunakan Extreme Learning Machine (ELM). J. Pengemb. Teknol. Inf. dan Ilmu Komput. vol. 5, no. 6, pp. 2792–2800.

Andriani, Y., Silitonga, H., Wanto, A. (2018). Analisis jaringan syaraf tiruan untuk prediksi volume ekspor dan impor migas di indonesia. Regist. J. Ilm. Teknol. Sist. Inf. vol. 4, no. 1, pp. 30–40. doi: 10.26594/register.v4i1.1157.

Utomo, W. C. (2023). Prediksi Pergerakan Saham BBRI ditengah Issue Ancaman Resesi 2023 dengan Pendekatan Machine Learning. J. Teknol. dan Manaj. Inform. vol. 9, no. 1, pp. 20–27. doi: 10.26905/jtmi.v9i1.9135.

Hyndman, R. J., Kostenko, A. V. (2007). Minimum sample size requirements for seasonal forecasting models. Foresight Int. J. Appl. Forecast. vol. 6, no. 6, pp. 12–15.

Karno, A. S. B. (2020). Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory). J. Inform. Inf. Secur. vol. 1, no. 1, pp. 1–8. doi: 10.31599/jiforty.v1i1.133.

Saputra, N. W., Insani, F., Agustian, S., Sanjaya, S. (2023). Penerapan Deep Learning Menggunakan Gated Recurrent Unit Untuk Memprediksi Harga Minyak Mentah Dunia. Build. Informatics, Technol. Sci. vol. 5, no. 1, pp. 86–94. doi: 10.47065/bits.v5i1.3552.

Hasanah, R. N., Ravie, R. P., Hadi Suyono, O. M. P. (2020). Comparison Analysis of Electricity Load Demand Prediction using Recurrent Neural Network (RNN) and Vector Autoregressive Model (VAR). 12th Int. Conf. Electr. Eng. ICEENG 2020 pp. 23–29. doi: 10.1109/ICEENG45378.2020.9171778.

Desvina, A. P. (2021). Pemodelan Vector Autoregressive (Var) untuk Data Jumlah Perceraian di Kota Pekanbaru. J. Sains Mat. dan Stat. vol. 7, no. 2, pp. 97–107. doi: 10.24014/jsms.v7i2.13765.

Goel, H., Melnyk, I., Oza, N., Matthews, B., Banerjee, A. (2016). Multivariate Aviation Time Series Modeling: VARs vs. LSTMs.

Zhu, K., Liu, H. (2022). Confidence intervals for parameters in high-dimensional sparse vector autoregression. Comput. Stat. Data Anal. vol. 168. doi: 10.1016/j.csda.2021.107383.

Billio, M., Casarin, R., Rossini, L. (2018). Bayesian nonparametric sparse vector autoregressive models. Math. Stat. Methods Actuar. Sci. Financ. MAF. vol. 203, pp. 155–160. doi: 10.1007/978-3-319-89824-7_29.

Wen, X., Li, W. (2023). Time Series Prediction Based on LSTM-Attention-LSTM Model. IEEE Access. vol. 11, no. May, pp. 48322–48331. doi: 10.1109/ACCESS.2023.3276628.

Rigopoulos, G. (2022). A long short-term memory algorithm-based approach for univariate time series forecasting with application to GDP forecasting. Int. J. Financ. Manag. Econ., vol. 5, no. 2, pp. 22–29. doi: 10.33545/26179210.2022.v5.i2.139.

Hamer, S., Sleeman, J., Stajner, I. (2023). Forecast-Aware Model Driven LSTM.

Kumar, S., Sharma, R., Tsunoda, T., Kumarevel, T., Sharma, A. (2021). Forecasting the spread of COVID-19 using LSTM network. BMC Bioinformatics. vol. 22, no. Suppl 6, pp. 1–9. doi: 10.1186/s12859-021-04224-2.

Wang, X. F., Zhang, Y. (2020). Multi-Step-Ahead Time Series Prediction Method with Stacking LSTM Neural Network. 3rd Int. Conf. Artif. Intell. Big Data, ICAIBD. pp. 51–55. doi: 10.1109/ICAIBD49809.2020.9137492.

Possumah, M. K., Rohmawati, A. A. (2020). Prediksi Harga Saham Menggunakan Vector Autoregressive (var) Non-stasioner (studi Kasus Saham Perusahaan Pt United Tractors Tbk). e-Proceeding Eng. vol. 7, no. 2, pp. 8361–8374.

Nicholson, W. B., Wilms, I., Bien, J., Matteson, D. S. (2020). High dimensional forecasting via interpretable vector autoregression. J. Mach. Learn. Res. vol. 21, pp. 1–52.

Raharja, P. A. (2021). Prediksi Harga Ethereum Menggunakan Metode Vector Autoregressive. J. Informatics, Inf. Syst. Softw. Eng. Appl. vol. 3, no. 2, pp. 71–79.

Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., Shao, L. (2023). Normalization Techniques in Training DNNs: Methodology, Analysis and Application. IEEE Trans. Pattern Anal. Mach. Intell. vol. 45, no. 8, pp. 10173–10196. doi: 10.1109/TPAMI.2023.3250241.

Selle, N., Yudistira, N., Dewi, C. (2022). Perbandingan Prediksi Penggunaan Listrik dengan Menggunakan Metode Long Short Term Memory (LSTM) dan Recurrent Neural Network (RNN). J. Teknol. Inf. dan Ilmu Komput. vol. 9, no. 1, pp. 155–162. doi: 10.25126/jtiik.2022915585.

Pangestu, R. A., et al. (2024). Comparative Analysis of Support Vector Regression and Linear Regression Models to Predict Apple Inc. Share Prices. vol. 7, no. 1, pp. 148–156.

Hewamalage, H., Ackermann, K., Bergmeir, C. (2023). Forecast evaluation for data scientists: common pitfalls and best practices. Data Min. Knowl. Discov. vol. 37, no. 2, pp. 788–832. doi: 10.1007/s10618-022-00894-5.

Duran, P. A., et al. (2024). Data Mining Untuk Prediksi Penjualan Menggunakan Metode Simple Linear Regression Data Mining for Clothing Sales Prediction Using Simple Linear Regression Method. vol. 13, no. 1, pp. 27–34, 2024. doi: 10.34148/teknika.v13i1.712.




DOI: https://doi.org/10.26905/jtmi.v10i1.13127

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