Article Per Year (5 Year)
p-Index From 2019 - 2024
1.978
P-Index
This Author published in this journals
All Journal Jupiter Jurnas Nasional Teknologi dan Sistem Informasi Dinamisia: Jurnal Pengabdian Kepada Masyarakat JTT (Jurnal Teknologi Terapan) Journal of Applied Science, Engineering, Technology, and Education Jurnal Generic Jurnal Abdi Masyarakat Indonesia Indonesian Journal of Computer Science ABDIKOM
Purwita Sari
Fakultas Ilmu Komputer, Universitas Sriwijaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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

Found 1 Documents
Search
Journal : Jupiter

 1 
Klasifikasi Arritmia pada Sinyal EKG menggunakan Deep Neural Network Bayu Wijaya Putra; Rahmat Fadli Isnanto; Purwita Sari; Ariansyah Saputra; M. Rudi Sanjaya; A. Noviar Satria Mukti
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 13 No 1 (2021): JUPITER Vol. 13 No. 1 April 2021
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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

Abstract

Abstrak Penelitian yang dikembangkan saat ini memfokuskan klasifikasi sinyal Electrokardiogram (EKG) pada gangguan arritmia detak jantung. Monitoring ini bertujuan agar dapat menjadi penanganan dini terhadap berbagai jenis gangguan arritmia. Klasifikasi yang diajukan dapat mengklasifikasi 9 jenis gangguan arritmia dengan menggunakan metode Deep Neural Network (DNN). Teknik preprosessing data pada sinyal EKG sebelum proses klasifikasi, yaitu segmentasi, normalisasi menggunakan normalize bound, dan fitur extraction dengan menggunakan autoencoder. Hasil menunjukkan bahwa metode yang digunakan mendapatkan nilai akurasi yang sangat baik sebesar 99.62% dan sensitivity about 97.18%. Kata kunci—EKG, Arritmia, Klasifikasi, Deep Neural Network  Abstract The research developed today focuses the classification of Electrocardiogram (ECG) signals on heart rate arritmia disorders. This monitoring aims to be an early treatment of various types of arritmia disorders. Using the Deep Neural Network (DNN) process, the proposed classification will identify 9 kinds of arrhythmia disorders. Preprocessing of the ECG signal data technique before the classification process, namely segmentation, normalization using bound normalization, and autoencoder extraction function. Results showed that the system used gained an outstanding 99.62 percent precision value and about 97.18 percent sensitivity. Kata kunci—ECG, Arrhytmia, Classifikation, Deep Neural Network
Co-Authors A. Noviar Satria Mukti Abson Hadi Aditya Putra Perdana Prasetyo Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Rifai Ali Ibrahim Ariansyah Saputra Badia Perizade Bayu Wijaya Putra Dedy Kurniawan Didik Susetyo Dwi Rosa Indah Dwi Rosa Indah Ermatita Ermatita Huda Ubaya Iman Saladin B. Azhar Izzati Millah Hanifah Kemahyanto Exaudi Kesuma, Lucky Indra M. Rudi Sanjaya Muhammad Fachurrozi Muhammad Ichsan Hadjri Nabillah Selva Setiawan Rahmad Fadli Isnanto Rahmat Alif Rahmat Alif Akbari Rahmat Fadli Isnanto Rahmat Fadli Isnanto Rido Zulfahmi Rizka Dhini Kurnia Rossi Passarella Roswitha Yemima Tiur Mediswati Sutarno Sutarno Tharisa Antya Perdani Yadi Utama Zunaidah Zunaidah