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Mahardika, Fajar
Politeknik Negeri Cilacap
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

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IoT-Based Smart Detector with SVM and XGBoost for Real-Time Child Growth Monitoring and Stunting Risk Prediction Mahardika, Fajar; Syafirullah, Lutfi; Nugroho, Adlan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5394

Abstract

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.
Co-Authors Abdillah, Muhammad Labib Abdul Hakim Prima Yuniarto Abdul Razak Naufal Abdul Razak Naufal Adam Prayogo Kuncoro Adelia Fitriani Ahmad Zulfan Akrim Teguh Suseno Al’ Amin, M. Al’ Amin, M. Amin, M. Al' Anang Martoyo Andik Wijanarko, Andik Arie Aryanto Aurora Dwi Khatulistian Bambang Sumantri, R Bagus Bambang Sumatri, Raden Bagus Desmana, Satriawan Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Fazar Sidik Guntur Subari Hadi Jayusman Hasan Basri Hendra Marcos I Putu Dody Suarnatha Ihsan Alparisi Inayatul Inayah Inayatur Robaniyyah Isep Saepudin Jayusman, Hadi Joko Purwanto Joko Purwanto Kabul Agus Purwanto Kholil Mustofa Lutfi Syafirullah M. Al ‘Amin M. Azwan Merani, Sania Galuh Miftahul Khoiri Mohammad AL AMIN Moniroh, Nur Muhammad Ghofinda Prasetia Muhammad Nur Faiz Muhammad Sandi Muhammad Yusril Ihzam Muhammad, Kukuh Muin, Muhammad Abdul Muin Noviana Safira Nugroho, Adlan Nur Azizah Nur Muniroh Nurul Alfiah Pari, Riki Aldi Prasetyanti, Dwi Novia Puji Cahniya Sari Purba, Angga Maulana Rahmawan Bagus Trianto Ratih Ratih ratih ratih Riyadi Purwanto Riyadi Purwanto Rizki Ripai Saepudin, Isep Samsuri Yahya Samsuri Yahya Setiawan, Endri Shandy, Sony Veri Sidik, Fazar Sitaresmi Wahyu Handani, Sitaresmi Wahyu Suhenda Suhenda Suhenda, Suhenda Suseno, Akrim Teguh Tundo, Tundo Widya Pratiwi Yahya, Samsuri