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

Authors

  • Suamanda Ika Novichasari Teknologi Informasi, Universitas Tidar, Indonesia https://orcid.org/0000-0002-5267-5072
  • Alifia Revan Prananda Teknologi Informasi, Universitas Tidar, Indonesia
  • Dhanang Suwidagdho Pendidikan Bahasa dan Sastra Indonesia, Universitas Tidar, Indonesia
  • Syifa Fauziah Pendidikan Guru Pendidikan Anak Usia Dini, Universitas Ngudi Waluyo, Indonesia
  • Vania Amelia Setya Wijaya Teknologi Informasi, Universitas Tidar, Indonesia
  • Otmar Shah Adam Teknologi Informasi, Universitas Tidar, Indonesia

DOI:

https://doi.org/10.31004/obsesi.v8i5.6131

Keywords:

stunting, PAUD, machine learning, SVM

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.

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References

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Published

25-10-2024

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How to Cite

Novichasari, S. I., Prananda, A. R., Suwidagdho, D., Fauziah, S., Setya Wijaya, V. A., & Adam, O. S. (2024). Using Support Vector Machines for Predicting and Mitigating Stunting in Early Childhood Education in Rural Semarang. Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini, 8(5), 1303–1311. https://doi.org/10.31004/obsesi.v8i5.6131

Issue

Vol. 8 No. 5 (2024)

Section

Articles

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