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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Journal of Information Systems Engineering and Business Intelligence Jurnal Pendidikan Agama Islam MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Tarbiyatuna : Kajian Pendidikan Islam DIMAR: Jurnal Pendidikan Islam Journal of Soft Computing Exploration Jurnal Pendidikan Islam Journal of Student Research Exploration Journal of Information System Exploration and Research Scientific Journal of Informatics Jurnal Ilmiah Riset dan Pengembangan
Rofik Rofik, Rofik
FITK UIN Sunan Kalijaga Yogyakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : International Journal of Advances in Intelligent Informatics

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Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE Unjung, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1627

Abstract

Parkinson's disease is one of the major neurodegenerative diseases that affect the central nervous system, often leading to motor and cognitive impairments in affected individuals. A precise diagnosis is currently unreliable, plus there are no specific tests such as electroencephalography or blood tests to diagnose the disease. Several studies have focused on the voice-based classification of Parkinson's disease. These studies attempt to enhance the accuracy of classification models. However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. This research aims to improve the accuracy of speech-based Parkinson's disease prediction by addressing class imbalance in the data and building an appropriate model. The proposed new model is to perform class balancing using SMOTE and build an ensemble voting model. The research process is systematically structured into multiple phases: data preprocessing, sampling, model development utilizing a voting ensemble approach, and performance evaluation. The model was tested using voice recording data from 31 people, where the data was taken from OpenML. The evaluation results were carried out using stratified cross-validation and showed good model performance. From the measurements taken, this study obtained an accuracy of 97.44%, with a precision of 97.95%, recall of 97.44%, and F1-Score of 97.56%. This study demonstrates that implementing the soft-voting ensemble-SMOTE method can enhance the model's predictive accuracy.
Co-Authors Abdul Aziz, Alif Alamsyah - Anggraini, Tasya Fitria Ardyani, Salma Shafira Fatya Aribah, Lutfina Artiyani, Fani Aulia, Reza Budi Prasetiyo, Budi Dwi Hermawan, Guntur Dwi Jayanti, Oni Endang Sugiharti, Endang Faaza, Muhammad Hakim, Ade Anggian Hakim, Roshan Aland Herlambang, M. Jumanto Jumanto, Jumanto Jumanto Unjung KA, Cecep Bagus Suryadinata Much Aziz Muslim Muqowim, Muqowim Musaadah, Khalimah Noorhaidi Hasan Nurul Hak Pertiwi, Dwika Ananda Agustina Pratidinal Jadid, Rosyid Riza Arifudin Sabarudin Sabarudin Simanjuntak, Robert Panca R. Tanga, Yulizchia Malica Pinkan