APPLICATION OF THE NEURAL NETWORK AUTOREGRESSIVE (NNAR) METHOD FOR FORECASTING THE VALUE OF OIL AND GAS EXPORTS IN INDONESIA
Abstract
Indonesia is one of the countries with the most diversity and abundant natural resources, consisting of many commodities, and has enormous trade potential with other countries The success of economic activity a country can be measured by the amount of economic growth that occurs in the country. A recession is when a country's economic condition is getting worse. Meanwhile, a recession in Indonesia is expected to occur in 2023. In a 2022 news issue written by the editorial team, tirto.id said that some experts say that if 2023 is a recession, the cause is due to a spike in inflation from the impact of the Russia-Ukraine conflict. It is known that the value of oil and gas exports affects the Indonesian economy. Any increase in the value of oil and gas exports will be followed by an increase in economic growth, and vice versa. However, over time, the value of oil and gas exports has decreased every year. Therefore, forecasting the value of oil and gas exports is needed so that the country's economic sector development strategy can be on target. In addition, oil and gas export forecasting is also needed to determine the distribution of goods exports that must be carried out. In this study, we forecast the value of oil and gas exports using the neural network autoregressive (NNAR) method. The choice of this method is made because there is no assumption of normality of the residuals and white noise like in autoregressive models. From the NNAR method, the best model results are obtained, namely NNAR (2,3) with a MAPE value of 11.75640%, which means that this model has very good forecasting performance.
Downloads
References
M. L. Purba, M. Sihombing, and J. Sihotang, “Analisis Pengaruh Ekspor Migas, Ekspor Non Migas dan Penanaman Modal Asing Terhadap Pertumbuhan Ekonomi Indonesia Tahun 2000-2019,” J Econ Bus, Vol. 2, No. 2, Pp. 40–51, Aug. 2021, Doi: 10.36655/Jeb.V2i2.557.
A. D. Yurioputra, “Impact of Foreign Direct Investment of Indonesia Investment Authority on Economic Growth: Strengthening National Economic Recovery to Overcome Global Recession In 2023,” Jurnal Pajak dan Keuangan Negara (PKN), vol. 4, no. 1S, pp. 404–413, 2022.
E. Nurhayati, “Analisis Pengembangan Ekspor Kayu Manis Indonesia,” Ecoplan, Vol. 3, No. 1, Pp. 1–11, 2020.
I. P. Astuti and F. J. Ayuningtyas, “Pengaruh Ekspor dan Impor Terhadap Pertumbuhan Ekonomi di Indonesia,” Jurnal Ekonomi & Studi Pembangunan, Vol. 19, No. 1, 2018, Doi: 10.18196/Jesp.19.1.3836.
J. H. V. Purba and A. Magdalena, “Pengaruh Nilai Tukar Terhadap Ekspor dan Dampaknya Terhadap Pertumbuhan Ekonomi Indonesia,” Derema Jurnal Manajemen, Vol. 12, No. 2, Pp. 285–295, 2017.
M. U. M. Putra and S. Damanik, “Pengaruh Ekspor Migas dan Non Migas Terhadap Posisi Cadangan Devisa di Indonesia,” Jurnal Wira Ekonomi Mikroskil, Vol. 7, No. 2, Pp. 245–254, 2017.
A. Himawan, “Kontribusi Sektor Industri Migas Ke PDB Nasional 62,67 Persen,” May 03, 2017. Https://Www.Suara.Com/Bisnis/2017/05/03/190456/Kontribusi-Sektor-Industri-Migas-Ke-Pdb-Nasional-6267-Persen (Accessed Aug. 25, 2023).
J. Fattah, L. Ezzine, Z. Aman, H. El Moussami, and A. Lachhab, “Forecasting of Demand Using ARIMA Model,” International Journal of Engineering Business Management, Vol. 10, Oct. 2018, Doi: 10.1177/1847979018808673.
W. T. Handoko and A. N. Handayani, “Forecasting Solar Irradiation on Solar Tubes Using the LSTM Method and Exponential Smoothing,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 3, pp. 649–660, 2023.
P. R. Winters, “Forecasting Sales by Exponentially Weighted Moving Averages,” Manage Sci, Vol. 6, No. 3, Pp. 324–342, 1960.
U. Azmi, S. Soehardjoepri, and I. M. H. Putri, “Application of neural network autoregression (NNAR) and ARIMA-GARCH based on interpolation for forecasting direct economic losses of earthquake in Indonesia,” in AIP Conference Proceedings, AIP Publishing, 2022.
M. As’ad and E. Farida, “Implementasi Jaringan Syaraf Tiruan untuk Memprediksi Inflasi Bulanan di Kota Malang,” Jurnal Ilmiah Komputasi, Vol. 18, No. 2, Pp. 101–106, 2019.
R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice. Otexts, 2018.
Y. K. Hartono, “Comparing Neural Network Autoregressive Method for Import Duty Revenue Forecasting,” Jurnal Perspektif Bea dan Cukai, Vol. 6, No. 1, P. 2022, 2022.
M. As’ad, Sujito, and S. Setyowibowo, “Peramalan Harga Emas Harian dengan Model Hibrida Double Exponensial Smoothing Holt’s dan Jaringan Syaraf Tiruan,” Jurnal Ilmiah Komputasi, Vol. 19, No. 1, Pp. 51–58, 2020, Doi: 10.32409/Jikstik.19.1.2723.
J. E. Cavanaugh and A. A. Neath, “The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements,” Wiley Interdiscip Rev Comput Stat, vol. 11, no. 3, p. e1460, 2019.
V. Desiyanti, Y. D. Rahayu, and R. Umilasari, “Analisa Perbandingan Metode DMA dan DES (Holt) Dalam Peramalan Harga GKP Ditingkat Petani,” Jurnal Smart Teknologi, Vol. 3, No. 5, Pp. 552–559, 2022.
J. J. M. Moreno, A. P. Pol, A. S. S. Abad, and B. C. C. Blasco, “Using the R-Mape Index as A Resistant Measure of Forecast Accuracy,” Psicothema, Vol. 25, No. 4, Pp. 500–506, 2013, Doi: 10.7334/Psicothema2013.23.
R. S. Pontoh, T. Toharudin, B. N. Ruchjana, F. Gumelar, F. A. Putri, M. N. Agisya, and R. E. Caraka, “Jakarta Pandemic to Endemic Transition: Forecasting Covid-19 Using NNAR and LSTM,” Applied Sciences (Switzerland), Vol. 12, No. 12, Jun. 2022, Doi: 10.3390/App12125771.
Copyright (c) 2024 Tarisya Permata Junita, Mujiati Dwi Kartikasari
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
