KLASIFIKASI 15 KELAS BEATS ARITMIA MENGGUNAKAN GATED RECURRENT UNIT (GRU)

AGUSTINA, MARDA TILA and Nurmaini, Siti (2023) KLASIFIKASI 15 KELAS BEATS ARITMIA MENGGUNAKAN GATED RECURRENT UNIT (GRU). Undergraduate thesis, Sriwijaya University.

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Abstract

Arrhythmia is a pattern of sudden fluctuations in heart rate from the normal heart rate. Cardiologists often have difficulty explaining electrocardiogram information when manually diagnosing cardiac arrhythmias. Gated Recurrent Unit (GRU) architecture is an effective method in processing time series type data. GRU has the advantage of optimizing the structure of LSTM network, GRU network only has two gate structures including update gate and reset gate which can solve the problem of long time interval prediction. Therefore, GRU aims to identify dependencies on various time scales. The data used is data containing ECG recordings of 48 patients in the MIT-BIH Arrhytmia Database. Classification of ECG signals using the Gated Recurrent Unit (GRU) approach by finding the most effective data model using the splitting method and the k-fold method. From the experiments conducted with the k-fold method, the best results were obtained with batch size parameters of 32 and learning rate of 0.00001, namely accuracy 99.92%, sensitivity 96.92%, specificity 99.90%, precision 93.38%, and f1-Score 92.88%. The GRU method successfully classifies arrhythmia signals.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	klasifikasi, GRU, aritmia
Subjects:	L Education > L Education (General) > L7-991 Education (General) Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Marda Tila Agustina
Date Deposited:	10 Aug 2023 06:07
Last Modified:	10 Aug 2023 06:07
URI:	http://repository.unsri.ac.id/id/eprint/126816

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