Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Nurhayati, Lutfiana Deka
Unknown Affiliation
Computer Science & IT

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

Found 1 Documents
Search

 1 
Impact of SMOTE and ADASYN on Class Imbalance in Metabolic Syndrome Classification Using Random Forest Algorithm Nurhayati, Lutfiana Deka; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10657

Abstract

Metabolic Syndrome is a collection of medical conditions that can increase the risk of stroke, cardiovascular disease, and type 2 diabetes. Early detection of this condition requires a machine learning model capable of accurate classification to support timely treatment. However, class imbalance in data often hampers the performance of classification algorithms, particularly in recognizing minority classes, namely individuals diagnosed with Metabolic Syndrome. This study aims to analyze the effect of applying the SMOTE and ADASYN data balancing techniques in classifying Metabolic Syndrome using the Random Forest algorithm. These algorithms were chosen for their ability to produce accurate predictions, although their performance can decline when faced with imbalanced class distributions. The results showed that the model without data balancing techniques achieved 86% accuracy with a minority class recall of 75%. The application of SMOTE increased accuracy to 91% and recall to 93%, while ADASYN achieved 92% accuracy and a minority class recall of 95%. These findings indicate that the ADASYN technique combined with the Random Forest algorithm provides significant performance improvements in the classification of Metabolic Syndrome on imbalanced data.
Co-Authors Majid Rahardi