Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Data Science Methods and Applications
Firdani, Fahri
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Library & Information Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analisis Klasifikasi Multikelas Obesitas Menggunakan Algoritma Decision Tree Classifier, Random Forest Classifier, dan Support Vector Classifier (SVC) Septa, Oon; Triyasri, Novita; Permata, Maharani Aulia; Salsabila, Aghitsna; Firdani, Fahri
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Obesity remains a significant global health challenge, making early classification and detection essential to minimize the risk of more serious degenerative diseases. This study compares three machine learning algorithms—Random Forest, Decision Tree, and Support Vector Classifier (SVC) to determine which model is most effective in predicting weight status categories and obesity risks. The dataset used includes physical features such as age, height, weight, and Body Mass Index (BMI). The research process encompasses data preprocessing stages, feature correlation analysis, data splitting, model training, and evaluation using various performance metrics.The research results indicate that Random Forest demonstrates the highest discriminative ability with an AUC of 0.99, showing perfect accuracy in distinguishing between obesity categories. Decision Tree provides identical results in terms of accuracy at 95.45%, but with a slightly lower AUC value of 0.96. Meanwhile, SVC yields competitive results with an accuracy of 81.82%, although its performance remains below the two tree-based models. Overall, this study demonstrates that ensemble methods such as Random Forest hold great potential for use as decision support systems in detecting and classifying obesity status more accurately and reliably.
Co-Authors Permata, Maharani Aulia Salsabila, Aghitsna Septa, Oon Triyasri, Novita