Skripsi

PENINGKATAN KINERJA KLASIFIKASI ARITMIA BERBASIS DEEP FEATURES DAN TEACHING-LEARNING BASED OPTIMIZATION

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Penilaian

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This study developed an arrhythmia classification model based on electrocardiogram (ECG) signals using time-domain RR-interval feature extraction techniques and the implementation of Teaching-Learning-Based Optimization (TLO) for feature selection. The datasets employed include the MIT-BIH Arrhythmia Database, MIT-BIH Normal Sinus Rhythm Database, MIT-BIH Atrial Fibrillation Database, Lobachevsky University Database, and QT Database. The research process began with signal pre-processing using discrete wavelet transform (DWT) for denoising, R-peak detection, and feature extraction of RR-interval parameters, namely mean_RR, std_RR, RMS_RR, NN50, pNN50, cv_RR, min_RR, and max_RR. TLO was employed for feature selection to identify the most relevant subset of features for classification. The models tested included CNN, DNN, LSTM, and BiLSTM, each evaluated under two scenarios: with and without TLO-based feature selection. Model performance was evaluated using metrics of accuracy, recall, precision, and specificity. The findings demonstrate that the application of TLO enhances the performance of most models. The DNN+TLO model achieved the highest test accuracy of 78%, followed by CNN+TLO and LSTM+TLO, both achieving 75%. Notable improvements were also observed in specificity, with DNN+TLO and CNN+TLO attaining 94% and 93%, respectively

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004159	T178640	T1786402025	Central Library (REFERENCE)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1786402025

Publisher

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

Collation

xiii, 97 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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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

TUTI

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