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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Teknologi International Journal of Advances in Intelligent Informatics Jurnal Teknik Informatika (JUTIF)
Fiftin Noviyanto
Program Studi Informatika, Fakultas Teknologi Industri Universitas Ahmad Dahlan
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

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Stacking-Based Support Vector Machine and Multilayer Perceptron for Dysarthria Detection Using MFCC Features Pujiyanta, Ardi; Noviyanto, Fiftin; Ismail, Taufiq
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5199

Abstract

The manual diagnosis of dysarthria is often time-consuming and requires the expertise of trained specialists, which can delay early intervention and treatment. This study aims to develop an automated detection system to improve diagnostic accuracy and efficiency. Mel-Frequency Cepstral Coefficients (MFCC) are used as the primary features, and three classification models are evaluated: Support Vector Machine (SVM), Multilayer Perceptron (MLP), and a stacking ensemble that combines both. The evaluation is conducted on a dataset of 240 audio samples. Experimental results show that the stacking ensemble achieves the highest performance, with an accuracy of 97.92%, surpassing SVM (95.83%) and MLP (93.75%). These findings highlight the significant potential of voice-based classification to accelerate dysarthria diagnosis, thus supporting clinical screening and speech therapy applications.
Co-Authors Ahmad Ashari Amaliyah, Mely Ardi Pujiyanta Danang Dwi Wahyu Jati Iis Wahyuningsih Jati, Danang Dwi Wahyu Mazari, Al Taufiq Ismail Tedy Setiadi