Comparison of Multiple Linear Regression and Random Forest Methods for Predicting National Rice Production in Indonesia
DOI:
https://doi.org/10.30871/jaic.v9i6.11398Keywords:
Race, Prediction, Linier Regretion, BPS, CommodityAbstract
Rice is a strategic commodity that plays an important role in maintaining national food security. However, rice production in Indonesia still fluctuates due to variations in harvest area, productivity, climate conditions, and differences in regional characteristics. This condition demands a predictive model capable of providing more accurate production estimates to support food policy planning. This research aims to predict national rice production by comparing two methods: Multiple Linear Regression and Random Forest Regression, using data from the Central Bureau of Statistics (BPS) and Nasa Power for the period 2018–2024. The analysis stages include data preprocessing, data exploration, categorical variable transformation, splitting data into training and testing sets, model training, and evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The research results show that harvested area is the most dominant factor influencing rice production, followed by productivity, year, and province. Based on the evaluation results, Random Forest provided the best performance with an MAE value of 40,599.94, an RMSE of 77,153.07, and an R² of 0.9991. The low error value and the proximity of the prediction to the actual data indicate that this model is better at capturing non-linear patterns and inter-regional variations compared to Multiple Linear Regression. Overall, Random Forest can be an effective method for predicting national rice production and can be further developed in subsequent research by incorporating climate variables or other external factors.
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