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All Journal Kitektro InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Green Intelligent Systems and Applications Jurnal INFOTEL Jurnal Polimesin
Yunidar Yunidar
Department Of Elect Rical Engineering And Computer, Engineering Faculty, University Of Syiah Kuala, Banda Aceh, Indonesia
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : JOIV : International Journal on Informatics Visualization

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Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis Melinda Melinda; Oktiana Maulisa; Nissa Hasna Nabila; Yunidar Yunidar; I Ketut Agung Enriko
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1219

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.
Co-Authors Achmi Yuliani Al Bahri Alfa Tirta Mufti Elizar Elizar Elizar Elizar Fahri Heltha I Ketut Agung Enriko Iskandar Hasanuddin Khairani Khairani M Fahrur Rozi Melinda Melinda Melinda Melinda Melinda Melinda Mohd. Syaryadhi Muhammad Furqan Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Nissa Hasna Nabila Nur Afny Catur Andryani Nurlida Basir Nurlida Basir Nurrizqa Nurrizqa Oktiana Maulisa Riska Sufina Rizal Munadi Rizal Syahyadi Rizki Azhari Shaquille Rizki Ramadhan Na Syahrial Syahrial Syahrial Yudha Nurdin Yuwaldi Away