Machine Learning-Based Early Detection of Stunting and Intervention Recommendations - Heti Mulyani; Musawarman Musawarman; Rifialdi Faturrohman; Daris Hafiz Permana (Komunitas Dosen Indonesia, 2025)
The stunting rate in Indonesia remains very high, one of the contributing factors being parents lack of knowledge about the symptoms of stunting and how to prevent it. Toddler checkups are only conducted once a month when health center staff are available, so parents are unable to detect stunting early on. This machine learning-based early stunting detection system offers a solution that allows parents to check for stunting in their toddlers at any time at home without having to wait for health center staff. Several previous studies have been conducted on stunting using machine learning, but they have not been integrated with expert systems for nutritional recommendations. The purpose of this study is to develop a machine learning-based early stunting detection system using a decision tree to quickly and accurately identify children at risk of stunting based on anthropometric indicators, namely height, age, and additional attributes such as gender. This study also aims to incorporate the knowledge of medical experts or nutritionists in the process of recommending interventions that parents should take. The model evaluation was conducted using the Confusion Matrix. Based on the research results from hybrid data obtained from the Sukasari community health center and Kaggle, the accuracy of the stunting classification model using decision trees was 98.7%. This model has been successfully implemented into a mobile-based application. Although the accuracy of this study is already high, it is hoped that future studies can be further improved by comparing other algorithms.