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KLASIFIKASI GAGAL JANTUNG KONGESTIF DENGAN OPTIMISASI PARAMETER LONG SHORT-TERM MEMORY MENGGUNAKAN ALGORITMA GRID SEARCH

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Congestive Heart Failure is a growing health problem with approximately 26 million adults worldwide having suffered from Congestive Heart Failure. Congestive heart failure generally occurs due to abnormalities in the heart muscles so that the heart cannot work normally, this can result in a lack of blood supply needed by the body. Classification of the ECG signal for Congestive Heart Failure automatically using deep learning can help doctors because of human errors in annotating the ECG signal manually. The method used in this research is Long Short-Term Memory (LSTM). LSTM is an effective method in processing time series data. In addition, LSTM can overcome vanishing and exploding gradient problems in RNN. In this study, there are two classification scenarios carried out, namely the unidirectional LSTM and Bi-LSTM models with parameter values optimized using a grid search algorithm including epoch, batch size, and learning rate resulting in a total of 40 models. Based on 40 models tested, the best classification model is Bi�LSTM with parameter values of 32 batch size, 0.0001 learning rate, and 200 epochs. The Bi-LSTM model has the highest evaluation results in the classification of ECG signals for Congestive Heart Failure with sensitivity, precision, specificity, accuracy and F1 values of 95.15%, 99.23%, 99.32%, 99.78%, and 99.69%.

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Inventory Code Barcode Call Number Location Status
2107002161T49784T497842021Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T497842021
Publisher
Inderalaya : Fakultas Ilmu komputer, Prodi Sistem Komputer.,
Collation
xix, 228 hlm,: ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.660 7
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Metode Transmisi Data
Prodi Sistem Komputer
Specific Detail Info
-
Statement of Responsibility
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