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

The Prevalence of Stunting in Sigi Regency remains notably high at 36.8%, significantly above the national target. Stunting is frequently caused by recurrent infections, poor sanitation, and chronic nutritional deficiencies. Since stunting is a condition of chronic malnutrition that impairs a child's physical and cognitive development, an early warning system is essential for prevention. This study proposes the development of a web-based application to predict the risk of stunting in vulnerable families. Families are the primary focus as they serve as the first environment where children grow and develop. If risk factors are present within a family, the likelihood of stunting increases. Therefore, early detection is crucial for mapping family health conditions. By predicting stunting risks, families can take preventive measures before the condition severely impacts the child. This early warning system serves as a critical alarm, encouraging families to be more vigilant in maintaining the health of all household members. The stunting prediction system is developed as a web-based application, utilizing 11 variables for early stunting detection and employing the K-Nearest Neighbor (K-NN) method. The model's accuracy is evaluated using a Confusion Matrix, achieving an accuracy rate of 99.991%.

Keywords: Early Warning System, Stunting, Classification, K-Nearest Neighbor, Confusion Matrix