Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Desy Marinda Oktavia Sitinjak
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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

Found 1 Documents
Search

 1 
Sistem Pendeteksi Premature Ventricular Contraction (PVC) berdasarkan Lebar QRS dan Gradien R menggunakan Metode FK-NN Desy Marinda Oktavia Sitinjak; Edita Rosana Widasari; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Heart is a vital organ and is the last line of defense for life and is a part of human body that has a role as a center for circulating blood. The heart's job is to pump blood to all body parts, where there is a rhythmic pattern of heartbeats when the heart pumps blood to all parts of the body. In a normal adult heart, it has a heartbeat ranging from 60-100 beats per minute. Adults who have a heart rate of fewer than 60 beats or more than 100 beats per minute mean there is a disturbance of their heartbeat or arrhythmia. One of example of an arrhythmia is Premature Ventricular Contraction (PVC). PVC conditions are common in humans, but if occur continuously it can increase the risk of heart disease. PVC can be prevented by early detection of heart disease, where an examination will be carried out using an ECG machine. However, the costs required to carry out an examination using an ECG machine are quite expensive. Regular early measurements are needed PVC using QRS Complex and R Gradient. The results of the AD8232 ECG sensor acquisition test get an error value of 7.14% with 5 tests. The accuracy results using the Fuzzy K-Nearest Neighbor (FK-NN) classification get 90% of the 20 test data used. For system computation time, it managed to reach 286.06 milliseconds.
Co-Authors Edita Rosana Widasari Utaminingrum, Fitri