Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques


Yudha Islami Sulistya(1*); Aina Musdholifah(2); Chrissandy Sapuletea(3); Elsi Titasari Br Bangun(4); Hizbullah Hamda(5); Sarah Anjani(6); Abednego Dwi Septiadi(7);

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Institut Teknologi Telkom
(4) Universitas Gadjah Mada
(5) Universitas Gadjah Mada
(6) Universitas Gadjah Mada
(7) Institut Teknologi Telkom
(*) Corresponding Author

  

Abstract


This research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest yields. The results show that ensemble learning techniques significantly improve prediction performance. For instance, the ensemble model for predicting area harvested, combining Model 6 (linear regression) and Model 10 (ARIMA), achieved  of coefficient of determination outperforming the individual models. Similarly, for predicting yield, the ensemble model combining Model 4 (linear regression) and Model 9 (ARIMA) achieved  of coefficient of determination indicating superior prediction accuracy. For predicting production, the ensemble model combining Model 2 (linear regression) and Model 8 (ARIMA) achieved  of coefficient of determination. These results demonstrate the effectiveness of ensemble learning in enhancing prediction accuracy with lower MSE and RMSE values. By analyzing various factors influencing rice yields, this research provides valuable insights for increasing rice production and yield, supporting efforts to improve the efficiency and effectiveness of rice farming, and contributing to achieving the United Nations Sustainable Development Goals (SDGs).


Keywords


Agricultural Efficiency; ARIMA; Ensemble Learning; Prediction; Rice Production; Rice Yield

  
  

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doi  https://doi.org/10.33096/ilkom.v16i2.1948.115-124 		  

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M. Lutfi, S. P. Agustin, and I. Nurma Yulita, “LQ45 Stock Price Prediction Using Linear Regression Algorithm, Smo Regression, and Random Forest,” in International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 267–271. doi: 10.1109/ICAIBDA53487.2021.9689749.

M. Skariah and C. D. Suriyakala, “Forecasting reservoir inflow combining Exponential smoothing, ARIMA, and LSTM models,” Arabian Journal of Geosciences, vol. 15, no. 14, pp. 1–11, Jul. 2022, doi: 10.1007/s12517-022-10564-x.

M. Kamble, S. Ghosh, and P. Patel, “Solar Irradiance Prediction using meteorological data by ensemble models,” in Proceedings of the International Database Engineering and Applications Symposium, IDEAS, 2020.

G. Srirutchataboon, S. Prasertthum, E. Chuangsuwanich, P. N. Pratanwanich, and C. Ratanamahatana, “Stacking Ensemble Learning for Housing Price Prediction: a Case Study in Thailand,” in 13th International Conference Knowledge and Smart Technology, Institute of Electrical and Electronics Engineers Inc., Jan. 2021, pp. 73–77. doi: 10.1109/KST51265.2021.9415771.

P. G. Jaiswal et al., “A Stacking Ensemble Learning Model for Rainfall Prediction based on Indian Climate,” in 6th International Conference on Information Systems and Computer Networks, ISCON 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 1–6. doi: 10.1109/ISCON57294.2023.10112077.

D. Sikka and D. Rajeswari, “Basketball Win Percentage Prediction using Ensemble-based Machine Learning,” in 6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 885–890. doi: 10.1109/ICECA55336.2022.10009313.

F. Meng and R. Dou, “Prophet-LSTM-BP Ensemble Carbon Trading Price Prediction Model,” Comput Econ, 2023, doi: 10.1007/s10614-023-10384-5.

P. Theerthagiri and A. U. Ruby, “Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends,” Multimed Tools Appl, vol. 82, no. 16, pp. 24485–24504, Jul. 2023, doi: 10.1007/s11042-023-14819-x.

Y. Guo, Y. Feng, F. Qu, L. Zhang, B. Yan, and J. Lv, “Prediction of hepatitis E using machine learning models,” PLoS One, vol. 15, no. 9, pp. 1–12, Sep. 2020, doi: 10.1371/journal.pone.0237750.

A. Durgapal and V. Vimal, “Prediction of Stock Price Using Statistical and Ensemble learning Models: A Comparative Study,” in 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/UPCON52273.2021.9667644.

M. Lutfi, S. P. Agustin, and I. Nurma Yulita, “LQ45 Stock Price Prediction Using Linear Regression Algorithm, Smo Regression, and Random Forest,” in International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 267–271. doi: 10.1109/ICAIBDA53487.2021.9689749.

P. Theerthagiri and A. U. Ruby, “Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends,” Multimed Tools Appl, vol. 82, no. 16, pp. 24485–24504, Jul. 2023, doi: 10.1007/s11042-023-14819-x.

S. I. Busari and T. K. Samson, “Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models,” Sci Afr, vol. 18, pp. 1–9, Nov. 2022, doi: 10.1016/j.sciaf.2022.e01404.

D. Nanthiya, S. B. Gopal, S. Balakumar, M. Harisankar, and S. P. Midhun, “Gold Price Prediction using ARIMA model,” in 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) , Institute of Electrical and Electronics Engineers (IEEE), Jun. 2023, pp. 1–6. doi: 10.1109/vitecon58111.2023.10157017.

S. Panigrahi, R. M. Pattanayak, P. K. Sethy, and S. K. Behera, “Forecasting of Sunspot Time Series Using a Hybridization of ARIMA, ETS and SVM Methods,” Sol Phys, vol. 296, no. 1, Jan. 2021, doi: 10.1007/s11207-020-01757-2.

R. K. Jagait, M. N. Fekri, K. Grolinger, and S. Mir, “Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA,” IEEE Access, vol. 9, pp. 98992–99008, 2021, doi: 10.1109/ACCESS.2021.3095420.

M. Singh, A. K. Jakhar, A. Juneja, and S. Pandey, “Machine learning based framework for cryptocurrency price prediction,” in 3rd International Conference on Secure Cyber Computing and Communications, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 31–36. doi: 10.1109/ICSCCC58608.2023.10176572.

Y. Wang et al., “Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition,” Sci Rep, vol. 11, no. 1, pp. 1–17, Dec. 2021, doi: 10.1038/s41598-021-00948-6.

C. H. Chien, A. J. C. Trappey, and C. C. Wang, “ARIMA-AdaBoost hybrid approach for product quality prediction in advanced transformer manufacturing,” Advanced Engineering Informatics, vol. 57, pp. 1–11, Aug. 2023, doi: 10.1016/j.aei.2023.102055.

Y. Gong and P. Zhang, “Commodity Price Analysis and Prediction Based on Ensemble Learning,” in 2nd International Conference on Networking Systems of AI, INSAI 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 199–204. doi: 10.1109/INSAI56792.2022.00045.

I. Sardar, M. A. Akbar, V. Leiva, A. Alsanad, and P. Mishra, “Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries,” Stochastic Environmental Research and Risk Assessment, vol. 37, no. 1, pp. 345–359, Jan. 2023, doi: 10.1007/s00477-022-02307-x.

A. Swaraj, K. Verma, A. Kaur, G. Singh, A. Kumar, and L. Melo de Sales, “Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India,” J Biomed Inform, vol. 121, pp. 1–11, Sep. 2021, doi: 10.1016/j.jbi.2021.103887.


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Copyright (c) 2024 Yudha Islami Sulistya, Chrissandy Sapuletea, Chrissandy Sapuletea, Elsi Titasari Br Bangun, Elsi Titasari Br Bangun, Hizbullah Hamda, Hizbullah Hamda, Sarah Anjani, Sarah Anjani

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