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All Journal International Journal of Electrical and Computer Engineering International Journal of Evaluation and Research in Education (IJERE) Journal of Educational, Health and Community Psychology Psikohumaniora: Jurnal Penelitian Psikologi Gadjah Mada Journal of Psychology Jurnal Pengukuran Psikologi dan Pendidikan Indonesia (JP3I) Jurnal Ilmiah Universitas Batanghari Jambi Emerging Science Journal Psikoislamedia : Jurnal Psikologi Jurnal Keperawatan Respati Yogyakarta Psikostudia : Jurnal Psikologi Jurnal Muara Ilmu Sosial, Humaniora, dan Seni Psyche: Jurnal Psikologi Pena Medika : Jurnal Kesehatan Jurnal Psikologi Universitas Gadjah Mada Gadjah Mada Journal of Professional Psychology (GamaJPP) Buletin Psikologi JNPM (Jurnal Nasional Pendidikan Matematika) Promotor: Jurnal Mahasiswa Kesehatan Masyarakat
Sri Kusrohmaniah, Sri
Fakultas Psikologi Universitas Gadjah Mada
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Journal : International Journal of Electrical and Computer Engineering

 1 
Galvanic Skin Response Data Classification for Emotion Detection Djoko Budiyanto Setyohadi; Sri Kusrohmaniah; Sebastian Bagya Gunawan; Pranowo Pranowo; Anton Satria Prabuwono
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (617.148 KB) | DOI: 10.11591/ijece.v8i5.pp4004-4014

Abstract

Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.
Fine tuning attribute weighted naïve Bayes model for detecting anxiety disorder levels of online gamers Latubessy, Anastasya; Wardoyo, Retantyo; Musdholifah, Aina; Kusrohmaniah, Sri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3277-3286

Abstract

This research applies the fine tuning attribute weighted naïve Bayes (FTAWNB) model using ordinal data. It is known that in previous research, the FTAWNB model outperformed its competitors on the dataset used. However, the FTAWNB model has not been applied in the mental health domain that uses ordinal data. Therefore, this research used the anxiety gamers dataset to test the fine-tuning attribute weighted Naïve Bayes (FTAWNB) model. Anxiety disorders are mental health disorders that can indicate the emergence of a gaming disorder. Gamers can experience anxiety disorders classified into four classes, namely minimal, mild, moderate, and severe anxiety. Then compare the results by FTAWNB obtained with three other naïve Bayes algorithms, namely Gaussian naïve Bayes, multinomial naïve Bayes, and categorical naïve Bayes, using the same dataset. Model performance is measured based on accuracy, precision, recall, and processing time. The test results show that the FTAWNB outperforms the other three models' accuracy, precision, and recall, with an accuracy value of 99.22%. While the accuracy of Gaussian NB is 91.132%, Categorical is 91.592%, and multinomial naïve Bayes is 61.104%. However, the FTAWNB takes slightly longer than the other three models' processing time. The FTAWNB takes 0.07 seconds to build the model and 0.05 seconds to test the model on training data.
Co-Authors Adi, Satriyo Priyo Afifah, Sabrina Puti Aina Musdholifah Akhmad Kurniawan Akhmad Kurniawan Ananda, Yuzi Putra Anastasya Latubessy Anggelina, Elsa Anisa Zahra Wijayanti Nugroho Anton Satria Prabuwono Apriasari, Hastinia Aristawati, Nina Vania Arrisha, Mutia Augustina Sulastri Danang Lelono Dharmadhyaksa, I Kadek Okta Djoko Budiyanto Setyohadi Fahira, Zihan Faizah, Niswatul Hafizhah, Nadia Fairus Halim, Angeline Freshbi Chesa Handy Aulia Zharfani Hanggoro, Debora Debby I Kadek Okta Dharmadhyaksa I Made Agus Wirawan I Made Agus Wirawan Irfantiara, Audiannisa Ismi Uswatun Khasanah Khasanah, Ismi Uswatun Laila Indra Lestari Lamanepa, Rosalia Katarina Latifatul Fazriyah Marvianto, Ramadhan Dwi Mubarok, Muhammad Abdullah Mufidah, Diffani Nani Restati Siregar Napitupulu, Kristin Citra Natasubagyo, Omar Syarief Nugroho, Anisa Zahra Wijayanti Omar Syarief Natasubagyo Pikoli, Amr Yazid Pramesti, Amelia Cahaya Pranowo Pranowo Primasari, Herdini Raharja, Belladina Nadyarinda Reformasianto, Viki Love Retantyo Wardoyo Salsabilla, Autasya Safira Savitri, Rizki Indriana Wulan Sebastian Bagya Gunawan Setiatama, Trisna Shellyna Fidya Silka Silka, Shellyna Fidya Sulastri, Augustina Supra Wimbarti Supra Wimbarti Supra Wimbarti Trisna Setiatama Wicaksono, Thomas Ari Widyanafi, Afina Rahmah Wikantyasning, Dhefira Elshafa Dyah Wimbrawati, Supra Yulius Sunardi Yusron, Isman Rahmani Yuzi Putra Ananda Zharfani, Handy Aulia Zulfikri Khakim