Analisis Perbandingan KNN, SVM, Decision Tree dan Regresi Logistik Untuk Klasifikasi Obesitas Multi Kelas
DOI:
https://doi.org/10.30865/klik.v4i6.1871Keywords:
Analysis; Decision Tree; KNN; Logistic Regression; Multiclass Classification; Obesity; SVMAbstract
Obesity has become a concerning global health issue, with continuously increasing prevalence. Early identification and accurate classification of obesity are crucial for implementing appropriate prevention and treatment strategies. This study aims to analyze and compare the performance of four popular classification algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, and Logistic Regression, in performing multi-class obesity classification based on Body Mass Index (BMI) according to World Health Organization (WHO) standards. Using a dataset reflecting population diversity, this research evaluates the ability of each algorithm to classify obesity into several categories, such as normal, overweight, obesity grade 1, obesity grade 2, and obesity grade 3. The study utilizes 2.111 records with 17 attributes. Results indicate that the Decision Tree Algorithm outperforms other algorithms, achieving an accuracy of 99.3%, precision of 0.97-1.00, recall of 0.98-1.00, and f1-score of 0.98-1.00. KNN follows with an accuracy of 99.0%, precision of 0.98-1.00, recall of 0.98-1.00 and f1-score of 0.98-1.00. meanwhile, the Logistic Regression algorithm achieves an accuracy of 98%, precision of 0.95-1.00, recall of 0.95-1.00, and f1-score of 0.95-1.00. SVM demontrates slightly lower performance, although still showing overall good results with an accuracy of 96.6%, precision of 0.90-0.99, recall of 0.94-1.00, and f1-score of 0.93-0.99..
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