APLIKASI DETEKSI STATUS STUNTING PADA BALITA MENGGUNAKAN GAUSSIAN NAIVE BAYES BERBASIS WEBSITE

Abstrak

Stunting is a term that refers to a serious health problem affecting the growth and development of children, especially toddlers aged 0–60 months. Without early detection, stunting can become chronic and lead to long-term effects on physical and cognitive development, as well as future productivity. Therefore, early detection is crucial to prevent and break the cycle of stunting. This study aims to implement a web-based stunting detection system that helps healthcare workers and parents identify stunting risks in children quickly and accurately. The main method used is the Naive Bayes Classifier, which was optimized to improve model performance. The dataset includes age (in months), gender, and height (in cm), which are classified into four categories: severely stunted, stunted, normal, and tall. The testing results show that the optimized model achieved an accuracy of 91% from 100 test data samples. This system is expected to support more accurate and timely decision-making for parents, medical professionals, and policymakers in early stunting prevention efforts.

Keywords: Detection, Stunting, Web, Gaussian Naive Bayes