Article Per Year (5 Year)
p-Index From 2021 - 2026
1.94
P-Index
This Author published in this journals
All Journal JurTI (JURNAL TEKNOLOGI INFORMASI) Journal of Information Systems and Informatics Infotek : Jurnal Informatika dan Teknologi Jurnal Ekonomi dan Teknik Informatika Abdi Wina : Jurnal Pengabdian Kepada Masyarakat Universitas Kristen Wira Wacana Sumba Jurnal Komputer, Informasi dan Teknologi Jurnal Publikasi Ilmu Komputer dan Multimedia
Winarnie
Universitas Satu
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Veterinary

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Machine Learning Classification of SCD, CHF, and NSR Using 15-Minute ECG-Derived HRV Features Febriyanti Panjaitan; Win Ce; M. Fajar Ramadhan; Winarnie; Hery Oktafiandi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1557

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection essential for effective intervention. Heart Rate Variability (HRV) is widely used as a non-invasive marker for assessing cardiac conditions, and machine learning has shown potential in classifying heart diseases such as Sudden Cardiac Death (SCD) and Congestive Heart Failure (CHF). This study evaluates the performance of Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN) using 15-minute ECG signals comprising three 5-minute segments. The dataset consists of 53 subjects, generating 159 segments, including SCD, CHF, and Normal Sinus Rhythm (NSR). To prevent data leakage, a subject-wise split (80:20) is applied for training and testing. Two evaluation scenarios are considered: per-segment classification and combined 15-minute classification. Results indicate that SVM and DT achieve consistently high, stable performance with near-perfect accuracy, precision, recall, and F1-score, whereas KNN shows lower, more variable performance, particularly in segment-based analysis. The combined 15-minute approach provides more stable results, suggesting improved HRV representation and class separability. Although the results are promising, further validation with larger, more diverse datasets is required to ensure robustness and generalizability. This study highlights the potential of HRV-based machine learning while emphasizing the importance of appropriate temporal representation and rigorous evaluation design.
Co-Authors Anggit Dwi Hartanto Ayuningtyas, Pratika Febriyanti Panjaitan Febriyanti Panjaitan Hery Oktafiandi Juwahir, Amat Khairu Nisa, Nisrina Kusrini M. Fajar Ramadhan M. Fajar Ramadhan Melani, Lestari Eka Mulyowati, Gandes Pebriyanti Panjaitan Putri, Farah Tisya Anindya Rahayu, Kurnia Puji Ramadhan, M. Fajar Sayid M. Raziq Olajuwon Win Ce Yohanes Yohanes