Work

Using Support Vector Machines for Predicting and Mitigating Stunting in Early Childhood Education in Rural Semarang

Year: 2024

Type: article

Abstract: In 2030, 70% of Indonesia's population will be of productive age (15-64 years), which is a demographic bonus. However, this potential is threatened by the high rate of stunting in children, which threatens future workforce productivity. Early identification of stunting risk is essential for timely intervention. This study develops a stunting prediction model using machine learning with data from early childhood education institutions in rural Semarang. The model used is a Support Vector Machine (SVM) implemented through the RapidMiner framework. The SVM model achieved an accuracy of 97.56%, a precision of 98.97%, a recall of 97.37%, and an AUC of 0.997. The results of the SVM model highlight the importance of physical motor skills and artistic development.

Source: Jurnal Obsesi Jurnal Pendidikan Anak Usia Dini

DOI: 10.31004/obsesi.v8i5.6131

Authors: Suamanda Ika Novichasari, Alifia Revan Prananda, Dhanang Suwidagdho, Syifa Fauziah, Vania Amelia Setya Wijaya, Otmar Shah Adam

Institution: Universitas Tidar

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDGs: Good health and well-being, Zero hunger

Open Access status: gold

Estimated APC paid: $32