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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
Andreas Panangian Tamba
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Computer Science & IT Engineering

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Klasifikasi Citra Elektrokardiogram untuk Deteksi Penyakit Jantung Menggunakan Metode GLCM dan SVM Andreas Panangian Tamba; I Gede Arta Wibawa
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p09

Abstract

Heart disease is a major cause of death worldwide. Electrocardiogram (ECG) is a common method used to detect heart abnormalities. Analyzing ECG signals requires expertise and can be time-consuming. This study investigated the use of machine learning to classify ECG images for heart disease detection. The proposed method utilizes Gray Level Co-occurrence Matrix (GLCM) for feature extraction such as Dissimilarity, contrast, energy, ASM, homogeneity and Correlation. Meanwhile using Support Vector Machine (SVM) for the classification. We achieved an accuracy of 99.61% using this approach. The results suggest that the combination of GLCM and SVM can be a valuable tool for ECG image classification and potentially aid in early and accurate diagnosis of heart disease. 
Co-Authors I Gede Arta Wibawa