KLASIFIKASI GANGGUAN IRAMA JANTUNG ARITMIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK 1-DIMENSI

HASSNI, NADHYA and Nurmaini, Siti (2021) KLASIFIKASI GANGGUAN IRAMA JANTUNG ARITMIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK 1-DIMENSI. Undergraduate thesis, Sriwijaya University.

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Abstract

Arrhythmias are considered to be the most frequently observed cases of cardiac abnormalities. Cardiac abnormalities such as arrhythmias affecting the electrical activity of the heart can be detected using an analysis of the ECG waveform that differs from the normal ECG waveform. Classification of ECG Arrhythmias automatically using deep learning can help doctors because of human errors in manually annotating ECG signals. 1 Dimensional CNN is commonly used to solve difficult image-based pattern recognition but with a simple and precise architecture. CNN 1 Dimensions has a very good performance with data processing related to image data, computer vision. In this study, the classification scenario carried out is on the 1 Dimensional CNN model with optimized parameter values including epoch, batch size, and learning rate resulting in a total of 22 models. Based on 22 tested models, the best classification model with parameter values of 64 batch size, 0.001 learning rate, and 200 epochs. The CNN 1 Dimension model has the highest evaluation results in the classification of arrhythmic heart rhythm disturbance signals with sensitivity, precision, accuracy and F1 values of 99.4%, 95%, 99% and 99.69%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Gangguan Jantung Aritmia, Elektrokardiogram, Klasifikasi, Convolutional Neural.
Subjects:	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:	Nadhya Hassni
Date Deposited:	19 Jan 2022 04:17
Last Modified:	19 Jan 2022 04:17
URI:	http://repository.unsri.ac.id/id/eprint/61824

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