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All Journal G-Tech : Jurnal Teknologi Terapan
Abdul Jalil
Universitas Ibrahimy, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Implementasi Algoritma Support Vector Machine Untuk Klasifikasi Status Stunting Pada Balita Abdul Jalil; Ahmad Homaidi; Zaehol Fatah
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4811

Abstract

In Indonesia, the main health problem is stunting or dwarfism in toddlers due to chronic malnutrition. This study uses the Support Vector Machine (SVM) algorithm to classify the stunting status of toddlers with high accuracy. The dataset for stunting cases in Indonesia, which includes 6500 data, consists of eight attributes: "Gender", "age", "birth weight", "body weight", "height", "exclusive action", "stunting" . This data was obtained from the public data Kaggle website. Preprocessing, such as data noise cleaning and data variable refinement, is a process stage. Next, the ready dataset is divided into twenty percent test data and eighty percent training data. The SVM algorithm that uses a "linear" kernel has an accuracy of 82%, precision of 80%, and recall of 86% when used to classify cases of stunting in toddlers. These results show that the SVM algorithm is very good at classifying stunting cases in toddlers. Next, the stunting classification model created was applied to a web application using the Streamlit framework.
Co-Authors Ahmad Homaidi Fatah, Zaehol