Comparison of ARIMA And NNAR Model for Rice Production Forecasting
Keywords:
ARIMA, Rice Production Forecasting, SMAPE, NNAR
Abstract
Accurate forecasting of rice production is crucial for ensuring future food security in specific regions. This study aimed to compare the predictive capabilities of two distinct time series models, Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR), in forecasting rice production. The selection of input variables for the NNAR model was aligned with the autoregressive components of the ARIMA model. Applying annual rice production data for the East Kalimantan Province from 1994 to 2023, this study evaluated forecasting accuracy through the Symmetric Mean Percentage Error (SMAPE). The results demonstrated the superior performance of the ARIMA ([2],0,0) model compared to the NNAR (2, 8) model. The advantage of the ARIMA ([2], 0, 0) model is attributed to the limited amount of data used, making simpler models like ARIMA more suitable for this case. Future research could develop more comprehensive forecasting models by incorporating additional factors such as weather data. Furthermore, the adopting of hybrid models combining ARIMA and NNAR, along with an increase in the quantity and diversity of data, could be potential steps towards enhancing the accuracy of rice production forecasting in the future.
Downloads
Download data is not yet available.
References
[1] P. S. Gita, “Pembatasan Alih Fungsi Lahan Pertanian Terhadap Rencana Pemindahan Ibu Kota Negara,” Ganaya: Jurnal Ilmu Sosial Dan Humaniora, vol. 2, no. 2, pp. 41–48, 2019, Accessed: Jul. 26, 2024. [Online]. Available: https://jayapanguspress.penerbit.org/index.php/ganaya/article/view/370
[2] K. N. Gunawan, “Analisis Pengaruh Jumlah Penduduk, Harga Beras dalam Negeri, dan Produksi Beras terhadap Impor Beras di Tahun 2000-2019,” Universitas Muhammadiyah Surakarta, Surakarta, 2021.
[3] R. Khairati and R. Syahni, “Respons Permintaan Pangan terhadap Pertambahan Penduduk di Sumatera Barat,” Jurnal Pembangunan Nagari, vol. 1, no. 2, pp. 19–36, 2016.
[4] N. Annamalai and A. Johnson, “Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach,” SN Comput Sci, vol. 4, no. 2, Mar. 2023, doi: 10.1007/s42979-022-01604-0.
[5] W. W. S. Wei, Time Series Analysis - Univariate and Multivariate Methods, Second edi. Boston: Pearson Addison Wesley, 2006.
[6] V. I. Kontopoulou, A. D. Panagopoulos, I. Kakkos, and G. K. Matsopoulos, “A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks,” Aug. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/fi15080255.
[7] R. Ospina, J. A. M. Gondim, V. Leiva, and C. Castro, “An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil,” Jul. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/math11143069.
[8] Z. Sun, “Comparison of Trend Forecast Using ARIMA and ETS Models for S&P500 Close Price,” ACM International Conference Proceeding Series, pp. 57–60, 2020, doi: 10.1145/3436209.3436894.
[9] S. S. Wulandari, Sufri, and S. Yurinanda, “Penerapan Metode ARIMA Dalam Memprediksi Fluktuasi Harga Saham PT Bank Central Asia Tbk,” BUANA Matematika: Jurnal Ilmiah Matematika dan Pendidikan Matematika, vol. 11, no. 1, pp. 53–68, 2021.
[10] Y. Ma, “Analysis and Forecasting of GDP Using the ARIMA Model,” Information Systems and Economics, vol. 5, no. 1, 2024, doi: 10.23977/infse.2024.050112.
[11] W. Li and Z. G. Zhang, “Based on time sequence of ARIMA model in the application of short-term electricity load forecasting,” ICRCCS 2009 - 2009 International Conference on Research Challenges in Computer Science, pp. 11–14, 2009, doi: 10.1109/ICRCCS.2009.12.
[12] A. Pratiwi, D. Safitri, and B. Warsito, “Peramalan Penumpang Pelayaran Dalam Negeri Di Pelabuhan Tanjung Priok Dengan Metode ARIMA Box-Jenkins Dan Metode Variasi Kalender Arimax,” Jurnal Statistika , vol. 6, no. 1, pp. 56–64, 2018, [Online]. Available: www.bps.co.id.
[13] G. Perone, “Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy,” European Journal of Health Economics, vol. 23, no. 6, pp. 917–940, 2022, doi: 10.1007/s10198-021-01347-4.
[14] M. Kalantari, “Forecasting COVID-19 pandemic using optimal singular spectrum analysis,” Chaos Solitons Fractals, vol. 142, Jan. 2021, doi: 10.1016/j.chaos.2020.110547.
[15] Z. Pala and R. Atici, “Forecasting Sunspot Time Series Using Deep Learning Methods,” Sol Phys, vol. 294, no. 5, p. 50, 2019, doi: 10.1007/s11207-019-1434-6.
[16] G. Vijayalakshmi, K. Pushpanjali, and Dr. A. M. Babu, “A comparison of ARIMA and NNAR models for production of rice in the state of Andhra Pradesh,” International Journal of Statistics and Applied Mathematics, vol. 8, no. 3, pp. 251–257, Jul. 2023, doi: 10.22271/maths.2023.v8.i3c.1041.
[17] A. S. Ahmar, P. K. Singh, R. Ruliana, A. K. Pandey, and S. Gupta, “Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India,” Forecasting, vol. 5, no. 1, pp. 138–152, Mar. 2023, doi: 10.3390/forecast5010006.
[18] W. Jannah, “Invigorasi Beberapa Benih Padi (Oryza Sativa L.) Varietas Lokal Kadaluarsa Menggunakan Ekstrak Tauge,” Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru, Pekanbaru, 2024.
[19] M. N. Fawaiq, A. Jazuli, and M. M. Hakim, “Prediksi Hasil Pertanian Padi di Kabupaten Kudus dengan Metode Brown’s Double Exponential Smoothing,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 4, no. 2, pp. 78–87, 2019, doi: https://doi.org/10.29100/jipi.v4i2.1421.
[20] M. A. P. Hutabarat, M. Julham, and A. Wanto, “Penerapan Algoritma Backpropagation dalam Memprediksi Produksi Tanaman Padi Sawah Menurut Kabupaten/Kota di Sumatera Utara,” Jurnal semanTIK, vol. 4, no. 1, pp. 77–86, 2018.
[21] H. W. Herwanto, T. Widiyaningtyas, and P. Indriana, “Penerapan Algoritme Linear Regression untuk Prediksi Hasil Panen Tanaman Padi,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 8, no. 4, pp. 364–370, 2019, Accessed: Jul. 28, 2024. [Online]. Available: https://journal.ugm.ac.id/v3/JNTETI/article/view/2563
[22] P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003, doi: 10.1016/S0925-2312(01)00702-0.
[23] J. G. De Gooijer and R. J. Hyndman, “25 years of time series forecasting,” Int J Forecast, vol. 22, no. 3, pp. 443–473, 2006, doi: 10.1016/j.ijforecast.2006.01.001.
[24] B. L. Bowerman, R. T. O’Connell, and A. B. Koehler, Forecasting, Time Series, and Regression: An Applied Approach. 4th Edition, . Duxbury Press, 2005.
[2] K. N. Gunawan, “Analisis Pengaruh Jumlah Penduduk, Harga Beras dalam Negeri, dan Produksi Beras terhadap Impor Beras di Tahun 2000-2019,” Universitas Muhammadiyah Surakarta, Surakarta, 2021.
[3] R. Khairati and R. Syahni, “Respons Permintaan Pangan terhadap Pertambahan Penduduk di Sumatera Barat,” Jurnal Pembangunan Nagari, vol. 1, no. 2, pp. 19–36, 2016.
[4] N. Annamalai and A. Johnson, “Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach,” SN Comput Sci, vol. 4, no. 2, Mar. 2023, doi: 10.1007/s42979-022-01604-0.
[5] W. W. S. Wei, Time Series Analysis - Univariate and Multivariate Methods, Second edi. Boston: Pearson Addison Wesley, 2006.
[6] V. I. Kontopoulou, A. D. Panagopoulos, I. Kakkos, and G. K. Matsopoulos, “A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks,” Aug. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/fi15080255.
[7] R. Ospina, J. A. M. Gondim, V. Leiva, and C. Castro, “An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil,” Jul. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/math11143069.
[8] Z. Sun, “Comparison of Trend Forecast Using ARIMA and ETS Models for S&P500 Close Price,” ACM International Conference Proceeding Series, pp. 57–60, 2020, doi: 10.1145/3436209.3436894.
[9] S. S. Wulandari, Sufri, and S. Yurinanda, “Penerapan Metode ARIMA Dalam Memprediksi Fluktuasi Harga Saham PT Bank Central Asia Tbk,” BUANA Matematika: Jurnal Ilmiah Matematika dan Pendidikan Matematika, vol. 11, no. 1, pp. 53–68, 2021.
[10] Y. Ma, “Analysis and Forecasting of GDP Using the ARIMA Model,” Information Systems and Economics, vol. 5, no. 1, 2024, doi: 10.23977/infse.2024.050112.
[11] W. Li and Z. G. Zhang, “Based on time sequence of ARIMA model in the application of short-term electricity load forecasting,” ICRCCS 2009 - 2009 International Conference on Research Challenges in Computer Science, pp. 11–14, 2009, doi: 10.1109/ICRCCS.2009.12.
[12] A. Pratiwi, D. Safitri, and B. Warsito, “Peramalan Penumpang Pelayaran Dalam Negeri Di Pelabuhan Tanjung Priok Dengan Metode ARIMA Box-Jenkins Dan Metode Variasi Kalender Arimax,” Jurnal Statistika , vol. 6, no. 1, pp. 56–64, 2018, [Online]. Available: www.bps.co.id.
[13] G. Perone, “Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy,” European Journal of Health Economics, vol. 23, no. 6, pp. 917–940, 2022, doi: 10.1007/s10198-021-01347-4.
[14] M. Kalantari, “Forecasting COVID-19 pandemic using optimal singular spectrum analysis,” Chaos Solitons Fractals, vol. 142, Jan. 2021, doi: 10.1016/j.chaos.2020.110547.
[15] Z. Pala and R. Atici, “Forecasting Sunspot Time Series Using Deep Learning Methods,” Sol Phys, vol. 294, no. 5, p. 50, 2019, doi: 10.1007/s11207-019-1434-6.
[16] G. Vijayalakshmi, K. Pushpanjali, and Dr. A. M. Babu, “A comparison of ARIMA and NNAR models for production of rice in the state of Andhra Pradesh,” International Journal of Statistics and Applied Mathematics, vol. 8, no. 3, pp. 251–257, Jul. 2023, doi: 10.22271/maths.2023.v8.i3c.1041.
[17] A. S. Ahmar, P. K. Singh, R. Ruliana, A. K. Pandey, and S. Gupta, “Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India,” Forecasting, vol. 5, no. 1, pp. 138–152, Mar. 2023, doi: 10.3390/forecast5010006.
[18] W. Jannah, “Invigorasi Beberapa Benih Padi (Oryza Sativa L.) Varietas Lokal Kadaluarsa Menggunakan Ekstrak Tauge,” Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru, Pekanbaru, 2024.
[19] M. N. Fawaiq, A. Jazuli, and M. M. Hakim, “Prediksi Hasil Pertanian Padi di Kabupaten Kudus dengan Metode Brown’s Double Exponential Smoothing,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 4, no. 2, pp. 78–87, 2019, doi: https://doi.org/10.29100/jipi.v4i2.1421.
[20] M. A. P. Hutabarat, M. Julham, and A. Wanto, “Penerapan Algoritma Backpropagation dalam Memprediksi Produksi Tanaman Padi Sawah Menurut Kabupaten/Kota di Sumatera Utara,” Jurnal semanTIK, vol. 4, no. 1, pp. 77–86, 2018.
[21] H. W. Herwanto, T. Widiyaningtyas, and P. Indriana, “Penerapan Algoritme Linear Regression untuk Prediksi Hasil Panen Tanaman Padi,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 8, no. 4, pp. 364–370, 2019, Accessed: Jul. 28, 2024. [Online]. Available: https://journal.ugm.ac.id/v3/JNTETI/article/view/2563
[22] P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003, doi: 10.1016/S0925-2312(01)00702-0.
[23] J. G. De Gooijer and R. J. Hyndman, “25 years of time series forecasting,” Int J Forecast, vol. 22, no. 3, pp. 443–473, 2006, doi: 10.1016/j.ijforecast.2006.01.001.
[24] B. L. Bowerman, R. T. O’Connell, and A. B. Koehler, Forecasting, Time Series, and Regression: An Applied Approach. 4th Edition, . Duxbury Press, 2005.
Published
2026-10-03
Section
Articles
Copyright (c) 2026 Mega Silfiani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.jpg)
