Pemanfaatan Data Sinyal Electroencephalogram (EEG) Pada Deteksi Stress Menggunakan Metode K-Nearest Neighbor (KNN)
Kata Kunci:
Stress, Electroencephalogram, Muse 2, Raspberry Pi, Fast Fourier Transform, K-Nearest NeighborAbstrak
Jurnal ini akan dipublikasikan pada Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI)
Referensi
World Health Organization, “Suicide,” 2019. [Online]. Available: >https://www.who.int/news-room/fact-sheets/detail/suicide>. [Accessed: 07 August 2023].
Kahn, “What You Should Know About Suicide,” 2019. [Online]. Available: <https://www.healthline.com/health/suicide-and-suicidalbehavior#suicidal-signs>. [Accessed: 07 August 2023].
Arsalan, A., Majid, M., Butt, A.R. and Anwar, S.M., 2019. Classification of Perceived Mental Stress Using A Commercially Available EEG Headband. IEEE Journal of Biomedical and Health Informatics, 23(6), pp.2257–2264. https://doi.org/10.1109/jbhi.2019.2926407.
TuerxunWaili, Alshebly, Y.S., Azami Sidek, K. and Md Johar, M.G., 2020. Stress recognition using Electroencephalogram (EEG) signal. Journal of Physics: Conference Series, 1502, p.012052. https://doi.org/10.1088/1742-6596/1502/1/012052.
Purnamasari, P.D. and Fernandya, A., 2019. Real Time EEG-based Stress Detection and Meditation Application with K-Nearest Neighbor. [online] IEEE Xplore. https://doi.org/10.1109/R10-HTC47129.2019.9042488.
Putra, A.P., Wiantari, N.W., Novita Dewi, P.M. and Bayu Atmaja Darmawan, I.D.M., 2019. Independent Component Analysis (ICA) Dan Sparse Component Analysis (SCA) Dalam Pemisahan Vokal Dan Instrumen Pada Seni Geguntangan. Jeliku. 8(1), p.105. https://doi.org/10.24843/jlk.2019.v08.i01.p13
Priya, T.H., Mahalakshmi, P., Naidu, V. and Srinivas, M., 2020. Stress detection from EEG using power ratio. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). https://doi.org/10.1109/ic-etite47903.2020.401.
Llerena, D., Delgado, R., Ubilluz, C. and Rolando, R., 2020. A prototype proposal for detection and reduction of Stress by using Brain waves and IoT. https://doi.org/10.1109/incodtrin51881.2020.00014.
Attaran, N., Puranik, A., Brooks, J. and Tinoosh Mohsenin, 2018. Embedded Low-Power Processor for Personalized Stress Detection. IEEE Transactions on Circuits and Systems Ii-express Briefs, 65(12), pp.2032–2036. https://doi.org/10.1109/tcsii.2018.2799821.
Roy, J.K., Das, A., Dutta, D., Sengupta, A., Ghosh, J. and Paul, S., 2014. ‘Intelligent Stress- Buster’- A labview based real-time embedded system for thought control using brain computer interface. https://doi.org/10.1109/indicon.2014.7030374.
Attar, E.T., 2022. Review of electroencephalography signals approaches for mental Stress assessment. Neurosciences, [online] https://doi.org/10.17712/nsj.2022.4.20220025.
Katsumata, S., Kanemoto, D. and Makoto, O., 2019. Applying Outlier Detection and Independent Component Analysis for Compressed Sensing EEG Measurement Framework. https://doi.org/10.1109/biocas.2019.8919117.
de Cheveigné, A. and Arzounian, D., 2018. Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data. NeuroImage, 172, pp.903–912. https://doi.org/10.1016/j.neuroimage.2018.01.035.
Ananya, R.K., S, I., Harimani, V., Mahesh Veezhinathan, M., B. Geethanjali, B. and Rajendran, B., 2020. Processing of EEG Signal for Classification of Epilepsy.https://doi.org/10.1109/iccsp48568.2020.9182271.
Jebelli, H., Khalili, M.M. and Lee, S., 2018. Mobile EEG-Based Workers’ Stress Recognition by Applying Deep Neural Network., pp.173–180. https://doi.org/10.1007/978-3-030-00220-6_21.
Guo, G., Wang, H., Bell, D., Bi, Y. and Greer, K., 2003. KNN Model-Based Approach in Classification. On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, 2888, pp.986–996. https://doi.org/10.1007/978-3-540-39964-3_62.
World Health Organization, “Suicide,” 2019. [Online]. Available: >https://www.who.int/news-room/fact-sheets/detail/suicide>. [Accessed: 07 August 2023].
Kahn, “What You Should Know About Suicide,” 2019. [Online]. Available: <https://www.healthline.com/health/suicide-and-suicidalbehavior#suicidal-signs>. [Accessed: 07 August 2023].
Arsalan, A., Majid, M., Butt, A.R. and Anwar, S.M., 2019. Classification of Perceived Mental Stress Using A Commercially Available EEG Headband. IEEE Journal of Biomedical and Health Informatics, 23(6), pp.2257–2264. https://doi.org/10.1109/jbhi.2019.2926407.
TuerxunWaili, Alshebly, Y.S., Azami Sidek, K. and Md Johar, M.G., 2020. Stress recognition using Electroencephalogram (EEG) signal. Journal of Physics: Conference Series, 1502, p.012052. https://doi.org/10.1088/1742-6596/1502/1/012052.
Purnamasari, P.D. and Fernandya, A., 2019. Real Time EEG-based Stress Detection and Meditation Application with K-Nearest Neighbor. [online] IEEE Xplore. https://doi.org/10.1109/R10-HTC47129.2019.9042488.
Putra, A.P., Wiantari, N.W., Novita Dewi, P.M. and Bayu Atmaja Darmawan, I.D.M., 2019. Independent Component Analysis (ICA) Dan Sparse Component Analysis (SCA) Dalam Pemisahan Vokal Dan Instrumen Pada Seni Geguntangan. Jeliku. 8(1), p.105. https://doi.org/10.24843/jlk.2019.v08.i01.p13
Priya, T.H., Mahalakshmi, P., Naidu, V. and Srinivas, M., 2020. Stress detection from EEG using power ratio. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). https://doi.org/10.1109/ic-etite47903.2020.401.
Llerena, D., Delgado, R., Ubilluz, C. and Rolando, R., 2020. A prototype proposal for detection and reduction of Stress by using Brain waves and IoT. https://doi.org/10.1109/incodtrin51881.2020.00014.
Attaran, N., Puranik, A., Brooks, J. and Tinoosh Mohsenin, 2018. Embedded Low-Power Processor for Personalized Stress Detection. IEEE Transactions on Circuits and Systems Ii-express Briefs, 65(12), pp.2032–2036. https://doi.org/10.1109/tcsii.2018.2799821.
Roy, J.K., Das, A., Dutta, D., Sengupta, A., Ghosh, J. and Paul, S., 2014. ‘Intelligent Stress- Buster’- A labview based real-time embedded system for thought control using brain computer interface. https://doi.org/10.1109/indicon.2014.7030374.
Attar, E.T., 2022. Review of electroencephalography signals approaches for mental Stress assessment. Neurosciences, [online] https://doi.org/10.17712/nsj.2022.4.20220025.
Katsumata, S., Kanemoto, D. and Makoto, O., 2019. Applying Outlier Detection and Independent Component Analysis for Compressed Sensing EEG Measurement Framework. https://doi.org/10.1109/biocas.2019.8919117.
de Cheveigné, A. and Arzounian, D., 2018. Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data. NeuroImage, 172, pp.903–912. https://doi.org/10.1016/j.neuroimage.2018.01.035.
Ananya, R.K., S, I., Harimani, V., Mahesh Veezhinathan, M., B. Geethanjali, B. and Rajendran, B., 2020. Processing of EEG Signal for Classification of Epilepsy.https://doi.org/10.1109/iccsp48568.2020.9182271.
Jebelli, H., Khalili, M.M. and Lee, S., 2018. Mobile EEG-Based Workers’ Stress Recognition by Applying Deep Neural Network., pp.173–180. https://doi.org/10.1007/978-3-030-00220-6_21.
Guo, G., Wang, H., Bell, D., Bi, Y. and Greer, K., 2003. KNN Model-Based Approach in Classification. On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, 2888, pp.986–996. https://doi.org/10.1007/978-3-540-39964-3_62.
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2024 Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Artikel ini berlisensiCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
