The Sriwijaya University Library

  • Home
  • Information
  • News
  • Help
  • Librarian
  • Login
  • Member Area
  • Select Language :
    Arabic Bengali Brazilian Portuguese English Espanol German Indonesian Japanese Malay Persian Russian Thai Turkish Urdu

Search by :

ALL Author Subject ISBN/ISSN Advanced Search

Last search:

{{tmpObj[k].text}}
Image of OPTIMISASI PARAMETER RECURRENT NEURAL NETWORK PADA PENDETEKSIAN ATRIAL FIBRILLATION MENGGUNAKAN ALGORITMA GRID SEARCH

Skripsi

OPTIMISASI PARAMETER RECURRENT NEURAL NETWORK PADA PENDETEKSIAN ATRIAL FIBRILLATION MENGGUNAKAN ALGORITMA GRID SEARCH

Wulandari, Putri - Personal Name;

Penilaian

0,0

dari 5
Penilaian anda saat ini :  

Atrial fibrillation (AF) is an abnormality in the rhythm of the human heartbeat that can cause stroke and heart failure. The process of detecting this disease is done by looking at and analyzing the morphology of the Electrocardiogram (ECG). This research uses the Deep Learning method because it can maximize the use of all information that comes from input, so that the decisions taken are stronger and better. For times series data, the Recurrent Neural Network (RNN) method is suitable for use. The types of RNN used are Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and also Gated Recurrent Unit (GRU) because they can overcome the vanishing gradient problem in RNN. There are two cases in this study, namely the use of imbalance data and balance data. The parameter tuning process used Grid Search algorithm by combining parameters such as learning rate, batch size and epoch to found the best model. The best model from each classifier will be tried again by adding a feature extraction process used Convolutional Neural Network (CNN). Based on the experimental results, the model that used the CNN feature extraction process has better results than the model without the feature extraction process. The model that has the best results was CNN-GRU model for data imbalance and data balance. On the imbalance data, the accuracy, sensitivity, specificity, precision, and F1 scores respectively 97.19%, 98.83%, 85.47%, 97.97% and 98.4%. On the balance data, the accuracy, sensitivity, specificity, precision, and F1 scores respectively 93.71%, 91.48%, 96.09%, 96.16%, and 93.76%.


Availability
Inventory Code Barcode Call Number Location Status
2107002424T49527T495272021Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T495272021
Publisher
Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021
Collation
xxv, 107 hlm,: ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
005.707
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Prodi Sistem Komputer
Data Sistem Komputer
Specific Detail Info
-
Statement of Responsibility
MURZ
Other version/related

No other version available

File Attachment
  • OPTIMISASI PARAMETER RECURRENT NEURAL NETWORK PADA PENDETEKSIAN ATRIAL FIBRILLATION MENGGUNAKAN ALGORITMA GRID SEARCH
Comments

You must be logged in to post a comment

The Sriwijaya University Library
  • Information
  • Services
  • Librarian
  • Member Area

About Us

As a complete Library Management System, SLiMS (Senayan Library Management System) has many features that will help libraries and librarians to do their job easily and quickly. Follow this link to show some features provided by SLiMS.

Search

start it by typing one or more keywords for title, author or subject

Keep SLiMS Alive Want to Contribute?

© 2025 — Senayan Developer Community

Powered by SLiMS
Select the topic you are interested in
  • Computer Science, Information & General Works
  • Philosophy & Psychology
  • Religion
  • Social Sciences
  • Language
  • Pure Science
  • Applied Sciences
  • Art & Recreation
  • Literature
  • History & Geography
Icons made by Freepik from www.flaticon.com
Advanced Search