Analysis of Food Security Index Predictions in Indonesia using Machine Learning Approach

Authors

DOI:

https://doi.org/10.37637/ab.v8i2.2302

Keywords:

data mining, model evaluation, food security and vulnerability atlas

Abstract

Food is one of the basic human needs that should be available at all times. To fulfill the role of in a region, the concept of food security is established to measure sufficiency, availability and quality of food. Food security for a country is expressed using Food Security Index (FSI). FSI score for a country reflects its ability for survival. It is therefore very important to measure the score and be able to predict future score to enable control and improvement. To realize the improvement of Indonesia's food security, a model is needed to predict the Food Security Index in Indonesia. This This paper explores the models using data from the Indonesian Food Security and Vulnerability Atlas (FSVA) at the Regency and City levels in 2018-2024 period with a total of 3,598 records. We evaluated Multiple Linear Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, eXtreme Gradient Boosting, Support Vector Regression, and Ensemble Machine Learning models for predicting the FSI score. The models are evaluated using r-squared (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results shows that the XGBoost method is the best method for predicting the Food Security Index in Indonesia with an R2 value of 0.912, RMSE of 0.053, and MAE of 0.037. Meanwhile, the ensemble machine learning method provides an R2 value of 0.79, RMSE of 0.083, and MAE of 0.063. In addition, the XGBoost method predicts the Food Security Index score in 2025 to be 75.56 and in 2026 to be 75.48.

Author Biographies

Frederic Morado Saragih, University of Indonesia

Computer Science

Wahyu Catur Wibowo, University of Indonesia

Computer Science

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Published

2025-07-06

How to Cite

Saragih, F. M., & Wibowo, W. C. (2025). Analysis of Food Security Index Predictions in Indonesia using Machine Learning Approach. Agro Bali : Agricultural Journal, 8(2), 377–393. https://doi.org/10.37637/ab.v8i2.2302

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Vol. 8 No. 2 (2025)

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