Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi
Wicaksono, Nikko Listio
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

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

Found 1 Documents
Search

 1 
Hyperparameter Optimization of Ensemble Learning for Heart Disease Prediction using Patient Data Wicaksono, Nikko Listio; Kusrini, Kusrini
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.6006

Abstract

This study evaluates the impact of hyperparameter optimization on the performance of four machine learning algorithms—Extra Trees, XGBoost, Random Forest, and AdaBoost—in heart disease prediction. The results show that hyperparameter tuning significantly improves model performance for three out of the four algorithms, with varying effects across models. Extra Trees demonstrates the most consistent improvement, achieving the highest Area Under the Curve (AUC) of 0.9107 and a recall of 80.93%, which is particularly crucial in medical contexts for accurately identifying disease cases. XGBoost exhibits the largest increase in accuracy, rising from 78.11% to 81.49%, while Random Forest shows improvements in both recall and F1-score. In contrast, AdaBoost experiences a slight decline in performance, suggesting that the model was already near optimal prior to tuning. Overall, Extra Trees with hyperparameter optimization emerges as the best-performing algorithm for heart disease prediction, offering high reliability in identifying at-risk patients.
Co-Authors Kusrini, Kusrini