A Toddler Health Monitoring System for Posyandu Using Support Vector Machine (Case Study: Puskesmas Nisam Antara)

Anggie Farradilla, Salahuddin Salahuddin, Safriadi Safriadi

Abstract


The advancement of information technology is exerting a considerable influence on various sectors in Indonesia, with healthcare representing a key area of application. Integrated Service Posts (Posyandu), which are instrumental in monitoring toddler growth and development, stand to benefit greatly. Nevertheless, their effectiveness is hampered by insufficient early detection of developmental disorders and a persistently high burden of nutritional issues, such as stunting and malnutrition. The province of Aceh, for instance, reports a stunting prevalence of 37%, a rate that notably exceeds the national average. This research aims to address these problems by implementing a Support Vector Machine (SVM) method for classifying the nutritional health of toddlers. The SVM method was selected for its proven performance in data analysis and classification. The SVM-based system is designed to process health parameters—such as weight, height, and head circumference—to provide accurate predictions regarding a child's growth and development status. The system was implemented via an information technology-based application that is easily accessible to healthcare workers and Posyandu volunteers. Evaluation using a confusion matrix demonstrated an accuracy rate of 97.92%. The model achieved a precision of 100%, recall of 97.72%, and an F1-score of 98.84% for the "good nutrition" category, with equally high performance for other categories. These results indicate that the system is highly effective at classifying toddler nutritional status. This research is expected to provide an innovative solution for supporting the early detection of child developmental disorders, improving the accuracy of toddler nutritional health evaluations, and serving as a reference for the development of similar technologies in the public health domain.

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