Prediction of Stunting Prevalence in Toddlers Using Support Vector Machine Algorithm and Synthetic Minority Oversampling Technique (SMOTE)
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Abstract
Stunting in toddlers represents a condition where isa nutritional deficiency. This becomes more complex when there is insufficient information regarding stunting in toddlers available. Predicting the prevalence of stunting in toddlers involves studying a dataset of stunting prevalence among toddlers through a supervised learning model using Support Vector Machine (SVM) and synthetic minority oversampling technique (SMOTE). The use of SMOTE serves as a data balancing method, while exploratory data analysis (EDA) acts as the preprocessing method for the toddler dataset. From the research implementation on a dataset consisting of 6879 toddlers, an accuracy of 94% was obtained for predictions. This accuracy is comprised of 94% accuracy, 95% precision, 94% recall, and a 94% F1-score.
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