IoT-Based Smart Detector with SVM and XGBoost for Real-Time Child Growth Monitoring and Stunting Risk Prediction - Mahardika, Fajar; Syafirullah, Lutfi; Nugroho, Adlan (Informatika, Universitas Jenderal Soedirman, 2026)
Stunting is a major public health issue, particularly in developing countries, causing long-term physical and cognitive impairments in children that reduce their quality of life and future productivity. To address this challenge, this study aims to develop an IoT-based smart detection system for child growth monitoring, enabling quicker and more accurate detection of stunting risks. The proposed system combines both hardware and intelligent software components to measure key growth indicators—height, weight, and BMI—using digital sensors and microcontrollers, transmitting the collected data to a cloud platform for real-time analysis. Machine learning algorithms, such as Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), are employed to predict stunting risk. Experimental results show that the XGBoost model outperforms SVM, achieving an accuracy of 80%, precision of 82%, recall of 78%, and F1-score of 79.9%, compared to SVM’s accuracy of 70%, precision of 68%, recall of 65%, and F1-score of 66.4%. This research provides a scalable technological framework for real-time stunting monitoring and early intervention, with the potential for implementation in resource-limited settings. By supporting national stunting reduction initiatives, the system enhances public health innovation and child welfare.