Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of New Media Technology
Abigail, Tesalonika
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Ensemble Learning - Random Forest Algorithm to Classify Obesity Level Abigail, Tesalonika; Overbeek, Marlinda Vasty
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3709

Abstract

Obesity is one of the serious global health problems caused by excessive accumulation of body fat. According to the World Health Organization (WHO), the prevalence of obesity has tripled in the last 40 years, with 650 million out of 1.9 billion overweight adults suffering from obesity. Obesity is a non-communicable disease that increases the risks of more dangerous diseases, such as heart disease and cancer. Therefore, early detection of obesity level is crucial. Currently, Body Mass Index (BMI) serves as a measurement indicator, but it tends to overestimate obesity for those with high muscle mass and vice versa, making it ineffective as it only relies on height and weight, without considering body composition and daily activities. To solve this, the best Random Forest model has been developed, selected based on the results of model selection after comparisons using feature selection and hyperparameter tuning. The selected model successfully improved accuracy by 1.4%, which then implemented into a web-based system to classify obesity levels. Evaluation of the model resulted in Precision, Recall, F1-Score, and Accuracy of 97%, 97%, 97%, and 96.8% respectively. Based on these evaluation results, it can be concluded that this system is highly effective in classifying obesity levels.
Ensemble Learning - Random Forest Algorithm to Classify Obesity Level Abigail, Tesalonika; Overbeek, Marlinda Vasty
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3709

Abstract

Obesity is one of the serious global health problems caused by excessive accumulation of body fat. According to the World Health Organization (WHO), the prevalence of obesity has tripled in the last 40 years, with 650 million out of 1.9 billion overweight adults suffering from obesity. Obesity is a non-communicable disease that increases the risks of more dangerous diseases, such as heart disease and cancer. Therefore, early detection of obesity level is crucial. Currently, Body Mass Index (BMI) serves as a measurement indicator, but it tends to overestimate obesity for those with high muscle mass and vice versa, making it ineffective as it only relies on height and weight, without considering body composition and daily activities. To solve this, the best Random Forest model has been developed, selected based on the results of model selection after comparisons using feature selection and hyperparameter tuning. The selected model successfully improved accuracy by 1.4%, which then implemented into a web-based system to classify obesity levels. Evaluation of the model resulted in Precision, Recall, F1-Score, and Accuracy of 97%, 97%, 97%, and 96.8% respectively. Based on these evaluation results, it can be concluded that this system is highly effective in classifying obesity levels.
Co-Authors Overbeek, Marlinda Vasty