Skripsi

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

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Penilaian

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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%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002424	T49527	T495272021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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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

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Classification

005.707

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Komputer
Data Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

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