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StuntCare: Digital Innovation for Early Warning of Stunting-Risk Families in Sigi Regency

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This study develops a web-based application predicting stunting risk in families, highlighting key factors like nutrition and health conditions.

The stunting prediction system achieves an accuracy rate of 99.991%, ensuring early detection and intervention.
Utilizing 11 variables, the early warning system focuses on family health to address the issue of chronic malnutrition.
The model is developed using the K-Nearest Neighbor method, evaluated through a Confusion Matrix.
Stunting affects 36.8% of children in Sigi Regency, emphasizing the need for timely preventive measures.

Abu et al. (2025) studied this question.

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This study develops a web-based application predicting stunting risk in families, highlighting key factors like nutrition and health conditions.