Forecasting The Value of Indonesian Oil-Non-Oil and Gas Imported Using The Gated Recurrent Unit (GRU)

Dian Kurniasari; Sulistian Oskavina; Wamiliana Wamiliana; Warsono Warsono

doi:10.24014/ijaidm.v6i1.20651

Forecasting The Value of Indonesian Oil-Non-Oil and Gas Imported Using The Gated Recurrent Unit (GRU)

Dian Kurniasari, Sulistian Oskavina, Wamiliana Wamiliana, Warsono Warsono

Abstract

In Indonesia, various factors play a role in economic development. Oil-non-oil and gas imports are one of the main factors. However, the value of oil-non-oil and gas imports in Indonesia fluctuates monthly. Therefore, an appropriate method is required to monitor changes in the value of oil-non-oil and gas imports in Indonesia so that the government can make the right choices. This study uses the GRU method to estimate the amount of oil-non-oil and gas imports in Indonesia. The best model for forecasting over the next two years has an optimum structure of 32 GRU units, 16 batch sizes, and 100 epochs, with a dropout of 0.2 and uses 80% training data and 20% test data. The MAPE value obtained is 0.999955%, with an accuracy of 99.000044%. Forecast results suggest an improvement from June 2022 to July 2024.

Keywords

Import, GRU, MAPE

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References

Hamdani and Haikal, Seluk Beluk Perdagangan Ekspor Impor, Edisi ke-2, Jakarta: Bushindo, 2018.

S. M. Shakeel, N. S. Kumar, P. P. Madalli, R. Srinivasaiah and D. R. Swamy, "COVID-19 Prediction Models: a Systematic Literature," Osong Public Health and Research Perspective, vol. 12, no. 4, pp. 215-229, 2021.

A. S. Bharatpur, "A Literature Review on Time Series Forecasting Methods," United Kingdom, 2022.

P. Lara-Benitez, M. Carranca-Garcia and J. C. Riquelme, "An Experimental Review on Deep Learning Architectures for Time Series Forecasting," International Journal of Neural Systems, vol. 31, no. 3, 2021.

J. F. Torres, D. Hadjout, A. Sebaa, F. Martinez-Alvarez and A. Troncoso, "Deep Learning for Time Series Forecasting: A Survey," Big Data, vol. 9, no. 1, pp. 3-21, 2021.

T. Katte, "Recurrent Neural Network and its Various Architecture Types," International Journal of Research and Scientific Innovation (IJRSI), vol. 5, no. 3, pp. 124-129, 2018.

K. Zarzycki and M. Lawrynczuk, "LSTMand GRUNeural Networks as Models of Dynamical Processes Used in Predictive Control: AComparison of Models Developed for Two Chemical Reactors," Sensors, vol. 21, no. 16, 2021.

M. V. Wuthrich and M. Merz, Statistical Foundations of Actuarial Learning and its Applications, Switzerland: Springer, 2023.

J. Chung, C. Gulcehre, K. Cho and Y. Bengio, "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling," arXiv: Neural and Evolutionary Computing, 2014.

X. H. Lie, H. H. Viet and G. Lee, "Application of Gated Recurrent Unit (GRU) Network for Forecasting River Water Levels Affected by Tides," in APAC 2019, Singapore, Springer Singapore, 2020, pp. 673-680.

A. Mathew, A. Arul and S. Sivakumari, "Deep Learning Techniques: An Overview," in Advanced Machine Learning Technologies and Applications, Singapore, Springer, 2020, pp. 599-608.

S. A. Patil, L. A. Raj and B. K. Singh, "Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model," Security and Communication Networks, vol. 2021, 2021.

M.-C. Lee, "Research on the Feasibility of Applying GRU and Attention Mechanism Combined with Technical Indicators in Stock Trading Strategies," Applied Sciences, vol. 12, no. 3, 2022.

A. Agarwal, P. Dey and S. Kumar, "Sentiment Analysis using Modified GRU," in IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing, New York, 2022.

J. Han, J. Pei and H. Tong, Data Mining Concepts and Techniques, 4th Edition, Elsevier, 2022.

S. K. C. Rudraraju, N. Desai, M. Krishna and B. S. B. P. Rani, "Data Mining in Cloud Computing: A Review," Journal of Advanced Research in Dynamical and Control Systems, vol. 9, 2019.

I. H. Sarker, "Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions," SN Computer Science, vol. 2, no. 6, pp. 1-20, 2021.

I. H. Sarker, M. H. Furhad and R. Nowrozy, "AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions," SN Computer Science, vol. 2, no. 3, pp. 1-18, 2021.

I. H. Sarker, "Machine Learning: Algorithms, Real-World Applications and Research Directions," SN Computer Science, vol. 2, no. 3, pp. 1-21, 2021.

X. Su, Y. Shan, C. Li, Y. Mi, Y. Fu and Z. Dong, "Spatial-temporal Attention and GRU Based Interpretable Condition Monitoring of Offshore Wind Turbine Gearboxes," IET Renewable Power Generation, vol. 16, no. 2, pp. 402-415, 2021.

D. V. Trivedi and S. Patel, "An Analysis of GRU-LSTM Hybrid Deep Learning Models for Stock Price Prediction," International Journal of Scientific Research in Science, Engineering and Technology, vol. 9, no. 3, pp. 47-52, 2022.

N. Li, L. Hu, Z.-L. Deng, T. Su and J.-W. Liu, "Research on GRU Neural Network Satellite Traffic Prediction Based on Transfer Learning," Wireless Personal Communications, vol. 118, no. 1, pp. 815-827, 2021.

C. Zeng, C. Ma, K. Wang and Z. Cui, "Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM," IEEE Access, vol. 10, pp. 47361-47370, 2022.

L. Amusa, D. North and T. Zewotir, "Optimal Hyperparameter Tuning of Random Forests for Estimating Causal Treatment Effects," Songklanakarin Journal of Science and Technology, vol. 43, no. 4, pp. 1004-1009, 2021.

E. K. Hashi and M. S. Uz Zaman, "Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction," Journal of Applied Science & Process Engineering, vol. 7, no. 2, pp. 631-647, 2020.

X. Xie, M. Xie, A. J. Mosyahedi and M. H. N. Skandari, "A Hybrid Improved Neural Networks Algorithm Based on L2 and Dropout Regularization," Mathematical Problems in Engineering, vol. 2022, pp. 1-19, 2022.

I. Kandel and M. Castelli, "The Effect of Batch Size on the Generalizability of the Convolutional Neural Networks on a Histopathology Dataset," ScienceDirect, vol. 6, no. 4, pp. 312-315, 2020.

I. Cardoza, J. P. Garcia-Vazquez, A. Diaz-Ramirez and V. Quintero-Rosas, "Convolutional Neural Networks Hyperparameter Tunning for Classifying Firearms on Images," Applied Artificial Intelligence, vol. 36, no. 1, 2022.

D. Kim, T. Kim, J. Jeon and Y. Son, "Soil-Surface-Image-Feature-Based Rapid Prediction of Soil Water Content and Bulk Density Using a Deep Neural Network," Applied Sciences, vol. 13, no. 7, pp. 1-14, 2023.

A. S. Santra and J.-L. Lin, "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting," Energies, vol. 12, no. 11, pp. 1-11, 2019.

Q. H. Nguyen, H.-B. Ly, S. L. Ho, N. Al-Ansari, H. V. Le, V. Q. Tran, I. Prakash and B. T. Pham, "Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil," Mathematical Problems in Engineering, vol. 2021, 2021.

C. Qi, A. Fourie, Q. Chen and Q. Zhang, "A Strength Prediction Model using Artificial Intelligence for Recycling Waste Tailings as Cemented Paste Backfill," Journal of Cleaner Production, vol. 183, pp. 566-578, 2018.

B. T. Pham, C. Qi, L. S. Ho, T. Nguyen-Thoi, N. Al-Ansari, M. D. Nguyen, H. D. Nguyen, H.-B. Ly, H. V. Le and I. Prakash, "A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil," Sustainability, vol. 12, no. 6, 2020.

U. I. Arfianti, D. C. R. Novitasari, N. Widodo, M. Hafiyusholeh and W. D. Utami, "Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm," IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 15, no. 2, pp. 141-152, 2021.

DOI: http://dx.doi.org/10.24014/ijaidm.v6i1.20651

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