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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Nurfiani, Indri
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Computer Science & IT Engineering

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PEMANFAATAN STFT DAN CNN DALAM PENGOLAHAN DATA SUARA UNTUK MENGKLASIFIKASIKAN SUARA BATUK Nurfiani, Indri; Jumadi, Jumadi; Deden Firdaus, Muhammad
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4729

Abstract

This research aims to develop an automatic cough sound evaluation system to improve the accuracy of respiratory disease diagnosis. In this study, the Short-Time Fourier Transform (STFT) and Convolutional Neural Network (CNN) methods were used to classify cough sounds into dry and wet coughs. The Naïve Bayes model was then used to identify respiratory diseases based on the cough classification results. Testing was conducted using the available cough sound dataset, resulting in a cough classification accuracy of 82% and a respiratory disease identification accuracy using Naïve Bayes of 71.43%. The evaluation results indicate that the developed system can accurately classify cough types and identify diseases. This system has the potential to enhance the prevention and management of respiratory diseases in resource-limited areas and can be a significant tool in medical practice for faster and more accurate diagnoses. Furthermore, this research opens opportunities for further development in disease detection and diagnosis technology through sound analysis, providing wide-ranging benefits for society and the healthcare sector.
Co-Authors Firdaus, Muhammad Deden Jumadi Jumadi