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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal INKOM Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal technoscientia Jurnal Teknologi Prosiding SNATIKA Vol 01 (2011) JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Information Technology and Computer Science Emerging Science Journal IJID (International Journal on Informatics for Development) CCIT (Creative Communication and Innovative Technology) Journal JISKa (Jurnal Informatika Sunan Kalijaga) JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Applied Data Sciences Makara Journal of Science Jurnal Informatika dan Multimedia JEECS (Journal of Electrical Engineering and Computer Sciences) Madani: Multidisciplinary Scientific Journal
Retantyo Wardoyo
Universitas Gadjah Mada
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : Emerging Science Journal

 1 
Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets Retantyo Wardoyo; I Made Agus Wirawan; I Gede Angga Pradipta
Emerging Science Journal Vol 6, No 2 (2022): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-02-013

Abstract

Several studies related to emotion recognition based on Electroencephalogram signals have been carried out in feature extraction, feature representation, and classification. However, emotion recognition is strongly influenced by the distribution or balance of Electroencephalogram data. On the other hand, the limited data obtained significantly affects the imbalance condition of the resulting Electroencephalogram signal data. It has an impact on the low accuracy of emotion recognition. Therefore, based on these problems, the contribution of this research is to propose the Radius SMOTE method to overcome the imbalance of the DEAP dataset in the emotion recognition process. In addition to the EEG data oversampling process, there are several vital processes in emotion recognition based on EEG signals, including the feature extraction process and the emotion classification process. This study uses the Differential Entropy (DE) method in the EEG feature extraction process. The classification process in this study compares two classification methods, namely the Decision Tree method and the Convolutional Neural Network method. Based on the classification process using the Decision Tree method, the application of oversampling with the Radius SMOTE method resulted in the accuracy of recognizing arousal and valence emotions of 78.78% and 75.14%, respectively. Meanwhile, the Convolutional Neural Network method can accurately identify the arousal and valence emotions of 82.10% and 78.99%, respectively. Doi: 10.28991/ESJ-2022-06-02-013 Full Text: PDF
Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition I Made Agus Wirawan; Retantyo Wardoyo; Danang Lelono; Sri Kusrohmaniah
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-03

Abstract

Participants' emotional reactions are strongly influenced by several factors such as personality traits, intellectual abilities, and gender. Several studies have examined the baseline reduction approach for emotion recognition using electroencephalogram signal patterns containing external and internal interferences, which prevented it from representing participants’ neutral state. Therefore, this study proposes two solutions to overcome this problem. Firstly, it offers a modified weighted mean filter method to eliminate the interference of the electroencephalogram baseline signal. Secondly, it determines an appropriate baseline reduction method to characterize emotional reactions after the smoothing process. Data collected from four scenarios conducted on three datasets was used to reduce the interference and amplitude of the electroencephalogram signals. The result showed that the smoothing process can eliminate interference and lower the signal's amplitude. Based on the three baseline reduction methods, the Relative Difference method is appropriate for characterizing emotional reactions in different electroencephalogram signal patterns and has higher accuracy. Based on testing on the DEAP dataset, these proposed methods achieved accuracies of 97.14, 99.70, and 96.70% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 89.71, 97.63, and 96.58% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 99.59, 98.20, and 99.96% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Doi: 10.28991/ESJ-2022-06-06-03 Full Text: PDF
Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition I Made Agus Wirawan; Retantyo Wardoyo; Danang Lelono; Sri Kusrohmaniah
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-09

Abstract

The convolution process in the Capsule Network method can result in a loss of spatial data from the Electroencephalogram signal, despite its ability to characterize spatial information from Electroencephalogram signals. Therefore, this study applied the Continuous Capsule Network method to overcome problems associated with emotion recognition based on Electroencephalogram signals using the optimal architecture of the (1) 1st, 2nd, 3rd, and 4th Continuous Convolution layers with values of 64, 128, 256, and 64, respectively, and (2) kernel sizes of 2×2×4, 2×2×64, and 2×2×128 for the 1st, 2nd, and 3rd Continuous Convolution layers, and 1×1×256 for the 4th. Several methods were also used to support the Continuous Capsule Network process, such as the Differential Entropy and 3D Cube methods for the feature extraction and representation processes. These methods were chosen based on their ability to characterize spatial and low-frequency information from Electroencephalogram signals. By testing the DEAP dataset, these proposed methods achieved accuracies of 91.35, 93.67, and 92.82% for the four categories of emotions, two categories of arousal, and valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 94.23, 96.66, and 96.05% for the four categories of emotions, the two categories of arousal, and valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 96.20, 97.96, and 97.32% for the four categories of emotions, the two categories of arousal, and valence, respectively. Doi: 10.28991/ESJ-2023-07-01-09 Full Text: PDF
Co-Authors Abdul Wahid Adiananda Adiananda Agus Harjoko Ahmad Ashari Ahmad Asharit Aina Musdholifah Aina Musdholifah Albert Dian Sano Anastasya Latubessy Andeka Rocky Tanaamah Andika Kurnia Adi Pradana Andriyani, Widyastuti Anny Kartika Sari Arief Kelik Nugroho, Arief Kelik Azhari Azhari Azhari Azhari Azhari Subanar Bambang Sugiantoro Bambang Sugiantoro Bangun Wijayanto Bernard Renaldy Suteja Budiarsa, Rahmat Christian Dwi Suhendra Clara Hetty Primasari Danang Lelono Decky Hendarsyah Desyandri Desyandri Djemari Mardapi Doni Setyawan E. Elsa Herdiana Murhandarwati Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Edi Winarko Edi Winarko Enny Itje Sela Gede Angga Pradipta, Gede Angga Hananto, Andhika Rafi Hardyanto Soebono Herri Setiawan Herri Setiawan I Made Agus Wirawan I Made Agus Wirawan Ida Ayu Putu Sri Widnyani Istiyanto, Jazi Eko Jazi Eko Istiyanto Jazi Eko Istiyanto Jazi Eko Istiyanto Joan Angelina Widians, Joan Angelina Khabib Mustofa Khairunnisa Khairunnisa Kusrini Kusrini Lausu, Suwandi Lilik Sumaryanti M Mustakim M.Cs S.Kom I Made Agus Wirawan . Moh Edi Wibowo Muhamad Munawar Yusro Muhammad Fakhrurrifqi Muhammad Mukharir Munakhir Mudjosemedi Mustakim, M Nola Ritha NUR HASANAH Peggi Sri Astuti Pratama, Kharis Suryandaru Purba, Susi Eva Maria Purwo Santoso Putri Elfa Mas`udia Rahman Erama Rahmat Budiarsa Ramos Somya Rika Rosnelly Rosa Delima Rosihan Rosihan, Rosihan Santoso, Purwo Silmina, Esi Putri Sri Andayani Sri Hartati Sri Hartati Sri Hartati Sri Hartati Sri Kusrohmaniah, Sri Sri Kusumadewi Sri Mulyana Subahar, Subahar Subanar . Suryo Guritno Suryo Guritno Suryo Guritno Tempola, Firman Tenia Wahyuningrum Wenty Dwi Yuniarti, Wenty Dwi Wibowo, Moh Edi Winarko, Edi Wiwiet Herulambang Yayi Suryo Prabandari