Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting

Wijayanti ; Ella Budi ; Setiadi ; De Rosal Ignatius Moses ; Setyoko ; Bimo Haryo
Journal of Computing Theories and Applications 📅 2024 Vol 1, No 3 🔗 DOI: 10.62411/jcta.10057
Abstract
Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.
Keywords
Harvest prediction; Paddy production forecasting; Regression analysis; Rice yield prediction; Rice production prediction; XGBoost Prediction
How to Cite

Wijayanti, et al. (2024). Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting. Journal of Computing Theories and Applications, 1(3). https://doi.org/10.62411/jcta.10057

Wijayanti, Ella Budi; Setiadi, De Rosal Ignatius Moses; Setyoko, Bimo Haryo, "Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting," Journal of Computing Theories and Applications, vol. 1, no. 3, 2024.

Wijayanti, Ella Budi; Setiadi, De Rosal Ignatius Moses; Setyoko, Bimo Haryo. "Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting." Journal of Computing Theories and Applications, vol. 1, no. 3, 2024.

Wijayanti, Ella Budi; Setiadi, De Rosal Ignatius Moses; Setyoko, Bimo Haryo. "Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting." Journal of Computing Theories and Applications 1, no. 3 (2024).

Wijayanti, et al. (2024) 'Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting', Journal of Computing Theories and Applications, 1(3). doi: 10.62411/jcta.10057.

Wijayanti, Ella Budi; Setiadi, De Rosal Ignatius Moses; Setyoko, Bimo Haryo. Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting. Journal of Computing Theories and Applications. 2024;1(3).

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