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This study delves into the application of machine learning techniques for predicting rice production in Indonesia, a country where rice is not just a staple food but also a key component of the agricultural sector. Utilizing data from 2018 to 2023, sourced from the Central Bureau of Statistics of Indonesia and the Meteorology, Climatology, and Geophysics Agency of Indonesia, this research presents a comprehensive approach to agricultural forecasting. The study begins with an Exploratory Data Analysis (EDA) to understand the variability and distribution of variables such as harvested area, production, rainfall, humidity, and temperature. Significant regional disparities in rice production are identified, highlighting the complexity of agricultural forecasting in Indonesia. Five machine learning models— Random Forest, Gradient Boosting, Decision Tree, Support Vector Machine, and Artificial Neural Network—are trained and tested. The Random Forest model stands out for its superior performance, as evidenced by the lowest Mean Squared Error (MSE) of 0.016186 and the highest R-squared (R2) of 0.850039, compared to the other models, indicating its high predictive accuracy and reliability. Hyperparameter tuning using the GridSearchCV technique was conducted on all five models, resulting in performance improvements across the board. Despite these enhancements, the Random Forest model remained the best, achieving a lower MSE of 0.014162 and a higher R2 value of 0.911257. This research not only underscores the effectiveness of machine learning in improving rice production predictions in Indonesia but also sets the stage for future research. It highlights the potential of advanced analytical techniques in enhancing agricultural productivity and decision-making, paving the way for further explorations into more sophisticated models and a broader range of data, ultimately contributing to the resilience and sustainability of Indonesia’s agricultural sector. |
