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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi Jupiter Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics ICON-CSE Annual Research Seminar PROCEEDING IC-ITECHS 2014 Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Linguistik Komputasional Sriwijaya Journal of Informatics and Applications Jurnal Generic JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pendidikan IPA Indonesia
Novi Yusliani
Department Informatics Engineering, Faculty Of Computer Science, Sriwijaya University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Physics Social Sciences Other

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Journal : Scientific Journal of Informatics

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Exploring Long Short-Term Memory and Gated Recurrent Unit Networks for Emotion Classification from Electroencephalography Signals Dian Palupi Rini; Winda Kurnia Sari; Novi Yusliani; Deris Stiawan; Aspirani Utari
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47734

Abstract

This study delves into comparing LSTM and GRU, two recurrent neural network (RNN) models, for classifying emotion data through electroencephalography (EEG) signals. Both models adeptly handle sequential data challenges, showcasing their unique strengths. In EEG emotion dataset experiments, LSTM demonstrated superior performance in emotion classification compared to GRU, despite GRU’s quicker training processes. Evaluation metrics encompassing accuracy, recall, F1-score, and area under the curve (AUC) underscored LSTM’s dominance, which was particularly evident in the ROC curve analysis. This research sheds light on the nuanced capabilities of these RNN models, offering valuable insights into their efficacy in emotion classification tasks based on EEG data. The study explores parameters, such as the number of layers, neurons, and the utilization of dropout, providing a detailed analysis of their impact on emotion recognition accuracy.Purpose: The proposed model in this study is the result of optimizing LSTM and GRU networks through careful parameter tuning to find the best model for classifying EEG emotion data. The experimental results indicate that the LSTM model can achieve an accuracy level of up to 100%.Methods: To improve the accuracy of the LSTM and GRU methods in this research, hyperparameter tuning techniques were applied, such as adding layers, dense layers, flattening layers, selecting the number of neurons, and introducing dropout to mitigate the risk of overfitting. The goal was to find the best model for both methods.Results: The proposed model in this study is capable of classifying EEG emotion data very effectively. The experimental results demonstrate that the LSTM model achieves a maximum accuracy of 100%, while the GRU model achieves a highest accuracy of approximately 98%.Novelty: The novelty of this research lies in the optimization of hyperparameters for both LSTM and GRU methods, leading to the development of novel architectures capable of effectively classifying EEG emotion data.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Ahmad Fali Oklilas Aini Nabilah Al Fatih, Zaky Alvi Syahrini Alvi Syahrini Utami Angelia, Nadya Anna Dwi Marjusalinah Annisa Darmawahyuni Ari Firdaus Ari Firdaus Ari Wedhasmara Ari Widodo Ariska, Meli Armansyah, Risky Armenia Yuhafiz Aruda, Syechky Al Qodrin Aspirani Utari Astero Nandito Ayu Purwarianti Az Zahra, Lutfiah Betharia Sri Fitrianti Danny Matthew Saputra Darmawahyuni, Annisa Darmawahyuni, Annisa Deris Stiawan Desty Rodiah Desty Roodiah Dhiya Fairuz Diah Kartika Sari Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Fadel Muhammad, Fadel Firdaus Firdaus Fitria Khoirunnisa Ghita Athalina Gilbert Christopher Jambak, Muhammad Ihsan Kanda Januar Miraswan Kartika, Diah Lidya Irfiyani Silaban M Fachrurrozi M. Fachrurrozi . Mastura Diana Marieska Melly Ariska Milka, Ikbal Adrian Muhammad Fachrurrozi Muhammad Fachurrozi Muhammad Naufal Rachmatullah Muhammad Omar Braddley Muhammad Raihan Habibullah Muhammad Rizqi Assabil Muharromi Maya Agustin Nur Hamidah Nurul Izzah Oktadini, Nabila Rizky Osvari Arsalan Plakasa, Gerald Rahma Haniffia Rahmannisa, Amanda Rahmat Fadli Isnanto Raisha Fatiya Reyhan Navind Shaquille Ridho Putra Sufa Rifka Widyastuti Rizki Kurniati Rizki Ramadandi Rusdi Efendi Saputra, Danny Mathew Saputra, Danny Matthew Sari, Tri Kurnia septi ana Siti Nurmaini Syechky Al Qodrin syechky al qodrin aruda Tiara Dewangga Tristi Dwi Rizki Wenty Octaviani Winda Kurnia Sari Yenny Anwar Yesi Novaria Kunang YUNITA Yunita Yunita Yunita Yunita Yunita Yunita