<![CDATA[Malnutrition among Indonesian toddlers remains a significant public health challenge, with the prevalence of stunting reaching 24.4% in 2021—indicating that nearly one in four children in Indonesia experiences substantial growth retardation at an early age. The long-term effects of stunting extend beyond physical growth limitations, encompassing reduced cognitive capacity, increased vulnerability to chronic diseases later in life, and diminished economic productivity. This research aims to develop a web-based application integrated with artificial intelligence (AI) and APIs to: (1) calculate height-for-age and weight-for-age Z-scores based on WHO references, (2) automatically identify the risk of stunting and nutritional status in toddlers, and (3) provide personalized nutritional interventions in the form of local food recommendations tailored to each individual’s condition. The application development follows the Agile Methodology. The final product is an AI-based API application for toddler nutritional assessment and intervention using WHO data as the foundation. The application was evaluated through validity and effectiveness testing. The system validity test resulted in a score of 0.83, indicating a valid classification, while the effectiveness test yielded a score of 0.82, reflecting a high level of effectiveness. Based on these findings, the application has the potential to serve as an innovative tool for healthcare providers and parents in both preventive and curative efforts against stunting, using a data-driven and culturally contextualized approach aligned with Indonesian conditions.]]>
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