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Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Articles

FORECASTING THE VALUE OF INDONESIA'S OIL AND GAS IMPORTS USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL

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  • Vera Maya Santi,
  • Rahadian Wahyu,
  • Ibnu Hadi
Vera Maya Santi
Statistics Study Program, Faculty of Mathematics and Natural Sciences, State University of Jakarta, Indonesia
Rahadian Wahyu
Mathematics Study Program, Faculty of Mathematics and Natural Sciences, State University of Jakarta, Indonesia
Ibnu Hadi
Mathematics Study Program, Faculty of Mathematics and Natural Sciences, State University of Jakarta, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss4pp2047-2058
Published December 19, 2023
Keywords
  • Indonesian Oil and Gas,
  • MAPE Value,
  • SARIMA,
  • Time Series
How to Cite
[1]
V. Santi, R. Wahyu, and I. Hadi, “FORECASTING THE VALUE OF INDONESIA’S OIL AND GAS IMPORTS USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL”, BAREKENG: J. Math. & App., vol. 17, no. 4, pp. 2047-2058, Dec. 2023.

Copyright (c) 2023 Vera Maya Santi, Rahadian Wahyu2, Ibnu Hadi

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

The value of Indonesia's oil and gas imports is a combination of the value of crude oil (petroleum), oil and natural gas products. Throughout 2021, the value of Indonesia's oil and gas imports reach US$ 25.53 billion or the equivalent of 382.95 trillion rupiah (estimated at US$ 1 = Rp. 15,000.00). The high demand for petroleum in Indonesia is due to the fact that petroleum is the main source of energy for daily life needs, especially for industrial, transportation and household needs. The requirment for oil imports is expected to increase along with the growth in Indonesia's population. Therefore, a step is needed to prevent an increase in the value of oil and gas imports in the coming period. One method of analysis that can be used is forecasting using the time series method with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The SARIMA model is a time series method with data that has a seasonal pattern and the forecasting results will get a pattern similar to the previous data. The data used is data on the monthly value of oil and gas imports from January 2005 to December 2022 with totaling 216 data. This research aims to find the best model and predict the value of Indonesia's oil and gas imports in the next 12 periods with data test in 4 periods (Januari to April 2023). The best model for the results of this research is (2, 1, 0)(0, 1, 1)43 with a MAPE value of 13.90%. Based on the accuracy of the MAPE value, this percentage has good quality forecasting results.

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References

  1. A. Redi, “Dinamika Konsepsi Penguasaan Negara Atas Sumber Daya Alam,” J. Konstitusi, vol. 12, no. 2, p. 401, 2016.
  2. Surtani, “Peran Serta Masyarakat dalam Pemanfaatan Sumber Daya Alam Secara Efektif dan Efisien Sumber Daya Alam Secara Efektif dan Efisien,” Pros. Semin. Nas. Geogr. 2016, p. 320, 2016, [Online]. Available: http://repository.unp.ac.id/653/1/SURTANI 18.pdf
  3. Kementrian ESDM, “Minyak dan Gas Bumi Semester I 2021,” Miny. dan Gas Bumi Semester I 2021, p. 104, 2021.
  4. V. B. Kusnandar, “Defisit Neraca Perdagangan Migas Indonesia Catat Rekor Terdalam pada Desember 2021,” https://databoks.katadata.co.id/datapublish/2022/01/19/defisit-neraca-perdagangan-migas-indonesia-catat-rekor-terdalam-pada-desember-2021, 2022.
  5. T. Purwanti, “Ini 5 Negara dengan Cadangan Minyak Terbesar di Dunia,” CNBC Indonesia, 2022. https://www.cnbcindonesia.com/market/20220912082219-17-371169/ini-5-negara-dengan-cadangan-minyak-terbesar-di-dunia
  6. E. M. Tamboesai, “Kajian Korelasi Genetika Geokimia Molekular Minyak Bumi Cekungan Sumatera Tengah, Riau,” J. Ind. Che. Acta, vol. 3, no. 1, pp. 5–10, 2012, [Online]. Available: https://ica.ejournal.unri.ac.id/index.php/ICA/article/view/913/906
  7. H. Feng, G. Duan, R. Zhang, and W. Zhang, “Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: Establishment of forecasting models using climate variables as predictors,” PLoS One, vol. 9, no. 1, pp. 1–10, 2014.
  8. M. Giovani, I. Anggriani, and S. A. W. D. Simatupang, “Comparison in Predicting the Short-Term Using the Sarima, Dsarima and Tsarima Methods,” BAREKENG J. Ilmu Mat. dan Terap., vol. 16, no. 4, pp. 1487–1496, 2022.
  9. T. C. Mills, “Time series modelling of temperatures: An example from Kefalonia,” Meteorol. Appl., vol. 21, no. 3, pp. 578–584, 2014.
  10. A. N. Munawaroh, “Peramalan Jumlah Penumpang pada PT. Angkasa Pura I (Persero) Kantor Cabang Bandar Udara Internasional Adisutjipto Yogyakarta dengan Metode Winter’s Exponential Smoothing dan Seasonal ARIMA,” Universitas Negeri Yogyakarta, 2010.
  11. N. AZIZAH, PERAMALAN JUMLAH BENCANA BANJIR DI INDONESIA MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA), vol. 2, no. 8.5.2017. 2022.
  12. W. Rahmalina and Novreta, “Peramalan Indeks Kekeringan Kelayang Menggunakan Metode Sarima dan SPI,” Potensi J. Sipil Politek., vol. 22, no. 1, pp. 64–75, 2020.
  13. A. Mishra, R. Morisetty, and R. Sarawagi, “Forecasting the production of Distillate Fuel Oil Refinery and Propane Blender net production by using Time Series Algorithms,” pp. 1–21, 2022, [Online]. Available: https://arxiv.org/ftp/arxiv/papers/2208/2208.05964.pdf
  14. B. P. Statistik, “Nilai Impor Migas-Non Migas (Juta US$), 2005-2022,” Badan Pusat Satistik. https://www.bps.go.id/indicator/8/1754/2/nilai-impor-migas-nonmigas.html (accessed Jun. 10, 2023).
  15. M. Fiecas, C. Leng, W. Liu, and Y. Yu, “Spectral analysis of high-dimensional time series,” Electron. J. Stat., vol. 13, no. 2, pp. 4079–4101, 2019.
  16. D. Grzesica and P. Wiȩcek, “Advanced Forecasting Methods Based on Spectral Analysis,” Procedia Eng., vol. 161, pp. 253–258, 2016.
  17. E. Ghaderpour, S. D. Pagiatakis, and Q. K. Hassan, “A survey on change detection and time series analysis with applications,” Appl. Sci., vol. 11, no. 13, 2021.
  18. P. Bloomfield, Fourier Analysis of Time : An Introduction, Second Edi. Canada: A Wiley-Interscience Publication, 2004. [Online]. Available: https://books.google.co.id/books?hl=id&lr=&id=zQsupRg5rrAC&oi=fnd&pg=PR11&dq=Spectral+analysis+is+also+a+form+of+Fourier+transform,+namely+a+form+that+converts+a+time+series+into+a+set+of+sine+or+cosine+waves+at+various+frequency+conditions.+It+can+be+use
  19. J. D. Scargle, “Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data,” Astrophys. J., vol. 263, pp. 835–853, [Online]. Available: https://adsabs.harvard.edu/full/1982ApJ...263..835S7
  20. A. H. Adineh, Z. Narimani, and S. C. Satapathy, “Importance of data preprocessing in time series prediction using SARIMA: A case study,” Int. J. Knowledge-Based Intell. Eng. Syst., vol. 24, no. 4, pp. 331–342, 2021.
  21. M. Othman, R. Indawati, A. A. Suleiman, M. B. Qomaruddin, and R. Sokkalingam, “Model Forecasting Development for Dengue Fever Incidence in Surabaya City Using Time Series Analysis,” Processes, vol. 10, no. 11, 2022.
  22. B. SENNAROĞLU and W. ZAYAT, “Performance Comparison of Holt-Winters and SARIMA Models for Tourism Forecasting in Turkey,” Doğuş Üniversitesi Derg., vol. 21, no. 2, pp. 63–77, 2020.
  23. Zulhamidi and R. Hardianto, “PERAMALAN PENJUALAN TEH HIJAU DENGAN METODE ARIMA (STUDI KASUS PADA PT. MK),” J. PASTI, vol. XI, no. 3, pp. 231–244, 2017.