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All Journal ComEngApp : Computer Engineering and Applications Journal
Setiadi, Raihan Mufid
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Computer Science & IT Engineering

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Classification of Atrial Fibrillation In ECG Signal Using Deep Learning Fachrurrozi, Muhammad; Rachmatullah, Muhammad Naufal; Setiadi, Raihan Mufid
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1- Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
A Hybrid of Fuzzy C-Means For The Segmentation In CT Scan and X-Ray Images For Screening The COVID-19 Patients WangNo, Nitit; Pichai, Supailin; Setiadi, Raihan Mufid
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 1 (2024)
Publisher : Universitas Sriwijaya

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In this paper, using CT scan and X-ray images, we present a hybrid approach, based on combining fuzzy C-means with k-means clustering, to evaluate and determine pneumonia infection caused by the coronavirus disease (COVID-19). To achieve this objective, we introduce a hybrid method that combines fuzzy C-means clustering with K-means clustering. This hybrid approach is designed to effectively segment object boundaries within medical images, enabling the precise identification of pneumoniarelated features. In addition to our hybrid method, we compare its performance with two other segmentation approaches: the Expectation Maximization (EM) algorithm and 2D Entropy segmentation. Which, the method we propose uses a comparison between the performances of the based on a database of medical imaging test. Experimental results showed that the proposed approach outperforms, it was found that the hybrid fuzzy C-means algorithm segmentation images methods give better performance in terms of accuracy, precision, and F-measure, which is effective in boundaries segmentation. Comparative results of the accuracy and image quality index demonstrate the robustness of AI. It also helps to improve work efficiency with accurate analysis of COVID-19 infection on CT scan and X-rays. In addition, the approach helps radiologists make clinical decisions for diagnosis, follow-up, and prognosis.
Co-Authors Muhammad Fachrurrozi Muhammad Naufal Rachmatullah Pichai, Supailin WangNo, Nitit