Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004252	T179336	T1793362025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1793362025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 175 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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