Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Pendidikan
Nugraha, I Gusti Agung Satria
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education Engineering

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

Found 1 Documents
Search

 1 
Application of Bagging Ensemble Learning on Naïve Bayes Algorithm to Predict Coronary Heart Disease Nugraha, I Gusti Agung Satria; Gunadi, I Gede Aris; Dewi, Luh Joni Erawati
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.981

Abstract

Cardiovascular health is vital, with heart disease, particularly Coronary Heart Disease (CHD), being a significant health concern in Indonesia. The 2023 Indonesian Health Survey reported 877,531 cases of heart disease. Traditional CHD diagnosis is often costly and invasive. Therefore, machine learning-based classification has emerged as a promising alternative for enhancing the accuracy and efficiency of detection. This study aims to predict CHD using a hybrid approach combining the Naïve Bayes algorithm with the Bagging ensemble method. Naïve Bayes was selected for its computational efficiency and effectiveness with high-dimensional data, while Bagging was employed to mitigate its inherent weaknesses by reducing variance and increasing prediction stability. The CRISP-DM methodology was applied to a secondary dataset of 462 rows from Kaggle. The research process included data preprocessing, method implementation, and evaluation using a confusion matrix. Results show the Bagging method with n=2 estimators achieved optimal performance, with 76.34% accuracy, 65.00% precision, and an f1-score of 70.27%. This study demonstrates that ensemble techniques can effectively improve the accuracy and stability of CHD prediction models, offering a reliable and low-cost solution for initial screening.
Co-Authors I Gede Aris Gunadi Luh Joni Erawati Dewi