Support Vector Machine-Based Classification of Toddler Stunting in Bandarharjo - Dalam bentuk pengganti sidang - Artikel Jurnal

DHAFA NUR FADHILAH

Informasi Dasar

27 kali
24.04.5971
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Karya Ilmiah - Skripsi (S1) - Reference

Stunting is a significant public health issue in Indonesia, with a prevalence of 21.6% in 2022, exceeding the WHO threshold of under 20%. This condition, resulting from chronic malnutrition, impairs physical growth and necessitates early detection for effective intervention. Machine learning is essential for this task, as it can analyze complex, high-dimensional data to uncover subtle patterns and risk factors that traditional methods might overlook. This study focuses on developing and evaluating a Stacked Support Vector Machine (SVM) model for predicting stunting risk in toddlers, and compares it with standard SVM models using linear, Radial Basis Function (RBF), and polynomial kernels on a dataset from Bandarharjo Health Center. The Stacked SVM model, which combines multiple weak learners to improve performance, showed superior results. By sequentially applying SVM models and correcting the errors of previous ones, Stacked SVM enhances overall predictive accuracy. This approach achieved 99.12% accuracy and an F1-score of 94.87%, outperforming linear SVM (98.86% accuracy, 94.24% F1-score), RBF SVM (98.68% accuracy, 92.51% F1-score), and polynomial SVM (98.59% accuracy, 92.22% F1-score). These findings highlight the effectiveness of Stacked SVM in improving early stunting detection and intervention, offering valuable insights for reducing stunting rates in Indonesia and informing future research.

Subjek

DATA SCIENCE
 

Katalog

Support Vector Machine-Based Classification of Toddler Stunting in Bandarharjo - Dalam bentuk pengganti sidang - Artikel Jurnal
 
 
 

Sirkulasi

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Tidak

Pengarang

DHAFA NUR FADHILAH
Perorangan
Putu Harry Gunawan
 

Penerbit

Universitas Telkom, S1 Informatika
Bandung
2024

Koleksi

Kompetensi

  • CCH3F3 - KECERDASAN BUATAN

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