ANALISIS EKSTRAKSI FITUR TIME-FREQUENCY DOMAIN UNTUK DETEKSI INFARK MIOKARD MENGGUNAKAN MACHINE LEARNING

AZ ZAHRAH, SANIA FATIMAH and Nurmaini, Siti (2025) ANALISIS EKSTRAKSI FITUR TIME-FREQUENCY DOMAIN UNTUK DETEKSI INFARK MIOKARD MENGGUNAKAN MACHINE LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

The healthcare industry in Indonesia is rapidly developing, particularly in addressing myocardial infarction (heart attack), a medical emergency that requires prompt detection. Common examinations such as ECG, blood tests, and clinical symptom analysis still rely heavily on manual assessment, which can be time-consuming. As a solution, a Machine Learning (ML)-based approach offers more efficient and automated detection. This study aims to improve the accuracy and efficiency of myocardial infarction detection by extracting features from ECG signals using time-frequency domain methods, namely STFT and DWT, and applying the SVM algorithm for classification. The results show an accuracy of 82% without denoising and 80% with denoising. This method has proven to be effective in identifying myocardial infarction from ECG signals compared to conventional methods.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Infark Miokard, Elektrokardiogram, Ekstraksi Fitur, Domain Waktu-Frekuensi, Machine Learning
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:	Sania Fatimah Az Zahrah
Date Deposited:	21 Jul 2025 05:47
Last Modified:	21 Jul 2025 05:47
URI:	http://repository.unsri.ac.id/id/eprint/179336

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