ANALISIS EKSTRAKSI FITUR FREQUENCY DOMAIN UNTUK KLASIFIKASI ABNORMALITAS JANTUNG MENGGUNAKAN MACHINE LEARNING

AMIRA, ZALFA and Nurmaini, Siti (2025) ANALISIS EKSTRAKSI FITUR FREQUENCY DOMAIN UNTUK KLASIFIKASI ABNORMALITAS JANTUNG MENGGUNAKAN MACHINE LEARNING. Undergraduate thesis, Sriwijaya University.

	Image RAMA_56201_09011282126111_cover.jpg - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (725kB)
	Text RAMA_56201_09011282126111.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_56201_09011282126111_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
	Text RAMA_56201_09011282126111_0002085908_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (654kB)
	Text RAMA_56201_09011282126111_0002085908_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (387kB) | Request a copy
	Text RAMA_56201_09011282126111_0002085908_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (406kB) | Request a copy
	Text RAMA_56201_09011282126111_0002085908_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011282126111_0002085908_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (217kB) | Request a copy
	Text RAMA_56201_09011282126111_0002085908_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (188kB) | Request a copy
	Text RAMA_56201_09011282126111_0002085908_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (752kB) | Request a copy

Abstract

Cardiac abnormalities are disorders in heart function that can be detected through electrocardiogram (ECG) signals. This research uses a frequency domain-based feature extraction method with Fast Fourier Transform (FFT), using ten features which then the data will be classified using machine learning algorithms, such as SVM, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN). Results show that Random Forest has the best performance with 100% accuracy on test data and 83% on validation data. Desicion Tree achieved 100% accuracy (test data) and 75% (validation data), KNN achieved 83% (test data) and 75% (validation data), while SVM only obtained 50% accuracy. The combination of feature extraction and appropriate algorithms proved effective in detecting cardiac abnormalities and can support a faster and more accurate diagnosis process.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Cardiac Abnormality, Electrocardiogram, Feature Extraction, Frequency Domain, 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:	Zalfa Amira
Date Deposited:	19 Jul 2025 06:34
Last Modified:	19 Jul 2025 06:34
URI:	http://repository.unsri.ac.id/id/eprint/179014

Actions (login required)

View Item