Grid Search-Optimized Artificial Neural Network Model for Rice Yield Prediction Using Weather and Soil Data in Malang City

priyanto, priyanto, Faisal, Muhammad ORCID: https://orcid.org/0000-0003-4884-7254 and Imamudin, Mochamad ORCID: https://orcid.org/0009-0006-7522-3710 (2025) Grid Search-Optimized Artificial Neural Network Model for Rice Yield Prediction Using Weather and Soil Data in Malang City. Engineering, Technology & Applied Science Research (ETASR), 15 (5). pp. 26487-26495. ISSN 2241-4487

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Abstract

This research optimizes an Artificial Neural Network (ANN) model using Grid Search (GS) for predicting
the rice yields in Indonesia. The purpose of this research was to enhance the performance of the ANN
model by systematically tuning its hyperparameters to improve its predictive accuracy. This research uses
the Multilayer Perceptron (MLP) method, and a comprehensive GS method was employed to explore
various hyperparameter combinations, including the number of hidden layers, activation functions,
solvers, regularization parameters, and learning rates. The optimization process involved evaluating each
hyperparameter configuration using cross-validation to select the best model based on performance
metrics, including the coefficient of determination (R²), Mean Absolute Error (MAE), and Mean Squared
Error (MSE). The study's results indicate that the optimized ANN model achieved an R² of 97.41%, MAE
of 766.69, and MSE of 1859857.06, outperforming the model without hyperparameters. This study
highlights the effectiveness of the GS optimization in enhancing the ANN model performance,
demonstrating that Hyperparameter Tuning (HT) is crucial for achieving improved prediction accuracy.
This study concludes that the ANN model can be optimized for practical use in predicting the rice yields, as
it shows strong performance.

Item Type:	Journal Article
Keywords:	artificial neural network; rice yield prediction; grid search
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	Muhammad Faisal
Date Deposited:	04 Dec 2025 22:24

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