| Issue |
BIO Web Conf.
Volume 171, 2025
The Frontier in Sustainable Agromaritime and Environmental Development Conference (FiSAED 2024)
|
|
|---|---|---|
| Article Number | 02011 | |
| Number of page(s) | 7 | |
| Section | Science and Technology for Sustainable Agromaritime | |
| DOI | https://doi.org/10.1051/bioconf/202517102011 | |
| Published online | 04 April 2025 | |
Evaluation of machine learning models based on household food insecurity data in Indonesia
1 School of Data Science, Mathematics, and Informatics, IPB University, Meranti Wing Street, Dramaga Campus IPB, Bogor, West Java, 16680, Indonesia
2 Faculty of Mathematics and Natural Sciences, Bengkulu University, Kandang Limun Street, Bengkulu, 38371, Indonesia
* Corresponding author: agusms@apps.ipb.ac.id
Household food insecurity is a critical issue, and accurate prediction models are essential for identifying at-risk households and guiding policy decisions to address this issue. This study compared the effectiveness and stability of two machine learning models: random forests (RF) and generalized random forests (GRF). Predicting household food insecurity using food insecurity experience scale data in West Java, Indonesia. The evaluation showed that the GRF model performed best and exhibited more consistent predictions. The important variables that influence household food insecurity in West Java are household size, type of house floor, bank savings account ownership, type of house wall, sanitation facility adequacy status, cash transfer program status, land ownership status, and food assistance recipient status.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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