Skripsi

PERANCANGAN SISTEM UNTUK MENDETEKSI NILAI QT-CORRECTED MENGGUNAKAN METODE LONG SHORT -TERM MEMORY

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Penilaian

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The bioelectric activity of the heart produces an electrical signal called an Electrocardiogram signal. This recording of electrical signals helps doctors diagnose abnormalities in the patient's heart. One form of congenital abnormality in the human heartbeat that can cause syncope, sudden cardiac arrest and sudden death is Long-QT syndrome, which is characterized by an extension of the interval between Q and T waves on an electrocardiogram signal. Classification of ECG beats in large amounts of data and sequences has its challenges, so Deep Learning that have the advantage in processing data automatically and are able to learn their own computational features and methods are highly recommended in this research. From the 3 experimental models, the best models were Bi-LSTM Model 3, with results of accuracy, sensitivity, specificity, precision, and F1-Score of 99.52%, 96.23%, 99.72%, 96.53%, 96.37% respectively. Then this model was tested back to the other datasets NSRDB with 99.76% accuracy results, 98.30 % sensitivity, 99.87% specificity, 98.37% precision, and 98.34% F1-Score.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002425	T40399	T403992021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T403992021

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021

Collation

xxii, 103 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

Other version/related

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