a Implementation of the Support Vector Machine Algorithm for Classifying Stunting Status in Toddlers
DOI:
https://doi.org/10.33379/gtech.v8i3.4811Keywords:
Stunting, Data Mining, SVM, kaggleAbstract
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.
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Copyright (c) 2024 Abdul Jalil, Ahmad Homaidi, Zaehol Fatah
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