Stunting remains a crucial health challenge in Indonesia, directly affecting the quality of future generations. This impaired growth condition caused by chronic malnutrition not only impacts a child's physical development, but also their cognitive abilities and future productivity. Facing this situation, East Java is now turning to artificial intelligence (AI) technology to accelerate prevention efforts.
A study published in Nonlinear Dynamics and Systems Theory (2025) attempts to address this challenge by building a stunting prevalence prediction model based on historical data from the Ministry of Home Affairs from 2021 to 2024. This approach aims to transform conventional, reactive methods into a more proactive and targeted strategy.
In the research, experts compared two machine learning methods: Support Vector Regression (SVR) and Decision Tree. The test results showed that the SVR method performed better with a lower RMSE (Root Mean Square Error) value of 0.1377, compared to 0.1642 for Decision Tree. SVR's advantage lies in its more stable ability to map complex relationships between health data variables.
The implementation of this technology is projected to have a significant impact on policymakers. With accurate predictive capabilities, local governments can prioritize interventions in areas identified as high-risk. This ensures that the allocation of health resources, education for pregnant women, and sanitation improvements are targeted and more efficient before stunting rates surge.
Although promising, researchers emphasize several challenges that must be addressed, such as integrating the system with regional health services, improving dataset quality, and increasing digital literacy among field workers. Moving forward, this model is expected to be developed more comprehensively by incorporating other supporting variables, such as poverty levels and access to clean water, to create a resilient stunting prevention system in East Java.
