relation: https://eprints.untirta.ac.id/45732/
title: Sistem Akuisisi dan Klasifikasi Aritmia Menggunakan Machine   Learning
creator: Rifqi Fauzi, Muhammad
subject: QA Mathematics
subject: QA75 Electronic computers. Computer science
subject: QA76 Computer software
subject: RZ Other systems of medicine
description: Arrhythmia is a disruption in heart rhythm that can indicate one of the deadliest   diseases in the world which is heart disease. Diagnosing arrhythmia is often done   by recording and analyzing signals using an ECG (Electrocardiogram). However,   ECG devices are expensive, and interpreting the waveforms requires specialized   expertise. Therefore, a compact, affordable, and portable device was developed to   acquire signals and classify arrhythmias. The Hilbert Transform is employed for   detecting R-peaks, which serve as annotation points for RR interval segmentation   during the preprocessing stage. These segmented intervals are then used as input   for arrhythmia classification using machine learning. The classification model   achieved an accuracy of 98.11%, with an average test accuracy of 98.2% on   respondent data.
date: 2025-01-24
type: Thesis
type: NonPeerReviewed
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identifier: https://eprints.untirta.ac.id/45732/1/Muhammad%20Rifqi%20Fauzi_3332190034_Fulltext.pdf
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identifier: https://eprints.untirta.ac.id/45732/2/Muhammad%20Rifqi%20Fauzi_3332190034_01.pdf
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identifier: https://eprints.untirta.ac.id/45732/3/Muhammad%20Rifqi%20Fauzi_3332190034_02.pdf
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identifier: https://eprints.untirta.ac.id/45732/9/Muhammad%20Rifqi%20Fauzi_3332190034_03.pdf
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identifier: https://eprints.untirta.ac.id/45732/10/Muhammad%20Rifqi%20Fauzi_3332190034_04.pdf
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identifier: https://eprints.untirta.ac.id/45732/5/Muhammad%20Rifqi%20Fauzi_3332190034_05.pdf
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identifier: https://eprints.untirta.ac.id/45732/6/Muhammad%20Rifqi%20Fauzi_3332190034_Ref.pdf
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identifier: https://eprints.untirta.ac.id/45732/7/Muhammad%20Rifqi%20Fauzi_3332190034_Lamp.pdf
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identifier: https://eprints.untirta.ac.id/45732/8/Muhammad%20Rifqi%20Fauzi_3332190034_CP.pdf
identifier:   Rifqi Fauzi, Muhammad  (2025) Sistem Akuisisi dan Klasifikasi Aritmia Menggunakan Machine Learning.  S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.