Skripsi
PREDIKSI RISIKO OBESITAS PADA DEWASA MUDA MENGGUNAKAN MACHINE LEARNING METODE DECISION TREE
Introduction : Obesity has been designated as a global epidemic due to its increasing prevalence. Obesity is a complex health problem with limited understanding of risk factors. The purpose of this study is to apply, analyze, and evaluate a Machine Learning algorithm, the Decision Tree in predicting obesity in young adults. Method : This type of research is analytic observational with a cross sectional study design. This study is a secondary study. The sample in this study were young adults (20-39 years old) who visited one of the health centers in Palembang City, with a minimum sample size of 262. Univariate and bivariate analysis using SPSS and multivariate using Machine Learning Decision Tree. Results : The total sample was 971 people with the results of 581 people (59.8%) being obese, the majority of respondents were female (73.7%), and the majority of samples aged 35-39 (45%). The results of the analysis with chi-square obtained a significant relationship in the variables of age, gender, and physical activity with a p-value
Inventory Code | Barcode | Call Number | Location | Status |
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2407000189 | T137027 | T1370272023 | Central Library (Referens) | Available but not for loan - Not for Loan |
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