Rancang Bangun Sistem Deteksi Emosi Stres menggunakan Metode Spectral Contrast berbasis Android
Kata Kunci:
Machine Learning, Spectral Contrast, Pemrosesan Suara, Deteksi Stres, CNNAbstrak
Stres yang muncul akibat ketidakseimbangan antara realitas dan harapan individu, dapat berdampak signifikan pada kesejahteraan seseorang, memengaruhi kesehatan mental, dan produktivitas. Penelitian ini membahas prevalensi masalah terkait stres di Indonesia dan minimnya kesadaran, serta perhatian terhadap gejala stres yang berujung pada pencegahan dan manajemen yang tidak memadai. Dengan memanfaatkan analisis spektral, khususnya Spectral Contrast yang dikombinasikan dengan algoritma Convolutional Neural Network (CNN) dalam pembelajaran mesin, penelitian ini mengembangkan sistem deteksi stres untuk perangkat berbasis Android. Penelitian ini mencakup wawasan dari penelitian sebelumnya yang menunjukkan bahwa dengan memanfaatkan metode Spectral Contrast, sistem dapat menganalisis data suara masukan dengan membandingkan amplitudo tertinggi dan terendah, serta memberikan penilaian stres yang lebih akurat. Sistem ini mencapai tingkat akurasi model sebesar 87,5%, dengan waktu komputasi rata-rata 2-3 detik. Selain itu, sistem menawarkan fungsionalitas real-time dan fitur yang mudah digunakan melalui aplikasi khusus yang terhubung ke basis data online.
Referensi
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Al Dujaili, M. J., Ebrahimi-Moghadam, A., & Fatlawi, A. 2021. Speech emotion recognition based on SVM and KNN classifications fusion. International Journal of Electrical and Computer Engineering, 11(2), 1-6. https://doi.org/10.11591/ijece.v11i2.pp1259-1264. University of Kufa.
Aljufri, M. N., & Prasetio, B. H. 2022. Sistem Deteksi Tingkat Stress Menggunakan Suara dengan Metode Jaringan Saraf Tiruan dan Ekstraksi Fitur MFCC berbasis Raspberry Pi. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 6(11), 1-8. Universitas Brawijaya.
Acevedo, E. O., & Ekkekakis, P. (Eds.). 2006. Psychobiology of Physical Activity. Champaign, IL: Human Kinetics.
Cox, R. H. 2002. Sport Psychology: Concepts and Applications (Edisi ke-4). New York: McGraw-Hill Co. Inc.
Jiang, D.-N., Lu, L., Zhang, H.-J., Tao, J.-H., & Cai, L.-H. 2002. Music Type Classification by Spectral contrast Feature. Universitas Tsinghua.
Kappen, M. 2022. Acoustic Speech Features In Social Comparison: How Stress Impacts The Way You Sound. Scientific Reports, 12, https://doi.org/10.1038/s41598-022-26375-9.
Khan Munna, & Sondhi Savita. 2015. Acoustic analysis of speech under stress. International Journal of Bioinformatics Research and Applications, 11(4), 417-429.
Kumar, S., & Thiruvenkadam, S. 2021. An Analysis of the Impact of Spectral contrast Feature in Speech Emotion Recognition. 1-9. Anna University.
Lake, D. 2004. Stress: How to Cope with Pressure. The Singapore Women’s Weekly Health Series. Singapore.
Lazzuardhy, D. A., & Prasetio, B. H. 2022. Sistem Pengenalan Intensitas Emosi Sedih melalui Ucapan menggunakan Ekstraksi Bark-Frequency Cepstral Coefficient dan K-Nearest Neighbor berbasis Raspberry Pi 4. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 6(11), 1-9. Universitas Brawijaya.
Liu, J. and Yu, J., 2011. Research on development of android applications. In: Proceedings - 2011 4th International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2011. pp.69–72. https://doi.org/10.1109/ICINIS.2011.40
Loizou, C. P. 2020. Voice signal analysis techniques for cognitive decline (stress) assessment. Journal of Physics: Conference Series, 1687(1), 012007. doi:10.1088/1742-6596/1687/1/012007
Mentari, A. Z. B., & Liana, E. 2020. Teknik Manajemen Stres yang Paling Efektif pada Remaja: Literature Review. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 12(4), 1-6. https://doi.org/10.52022/jikm.v12i4.69. UPNV Jakarta.
Sigmund, M. (2013). Information Technology and Control. Statistical Analysis of Fundamental Frequency Based Features in Speech under Stress, 42(13), 3895. http://dx.doi.org/10.5755/j01.itc.42.3.3895
Pervaiz, M., & Ahmed, S. 2016. Emotion Recognition from Speech using Prosodic and Linguistic Features. Retrieved from https://www.researchgate.net/publication/309624815_Emotion_Recognition_from_Speech_using_Prosodic_and_Linguistic_Features
Subbarao, V., & Vishnu, S. 2021. Speech Emotion Recognition Using K-Nearest Neighbor Classifiers. Dalam Recent Advances in Artificial Intelligence and Data Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-3342-3_10.
Adewumi, N.B., 2019. A Microcontroller Based Speech Recognition System Using MFCC Algorithm.
Al Dujaili, M. J., Ebrahimi-Moghadam, A., & Fatlawi, A. 2021. Speech emotion recognition based on SVM and KNN classifications fusion. International Journal of Electrical and Computer Engineering, 11(2), 1-6. https://doi.org/10.11591/ijece.v11i2.pp1259-1264. University of Kufa.
Aljufri, M. N., & Prasetio, B. H. 2022. Sistem Deteksi Tingkat Stress Menggunakan Suara dengan Metode Jaringan Saraf Tiruan dan Ekstraksi Fitur MFCC berbasis Raspberry Pi. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 6(11), 1-8. Universitas Brawijaya.
Acevedo, E. O., & Ekkekakis, P. (Eds.). 2006. Psychobiology of Physical Activity. Champaign, IL: Human Kinetics.
Cox, R. H. 2002. Sport Psychology: Concepts and Applications (Edisi ke-4). New York: McGraw-Hill Co. Inc.
Jiang, D.-N., Lu, L., Zhang, H.-J., Tao, J.-H., & Cai, L.-H. 2002. Music Type Classification by Spectral contrast Feature. Universitas Tsinghua.
Kappen, M. 2022. Acoustic Speech Features In Social Comparison: How Stress Impacts The Way You Sound. Scientific Reports, 12, https://doi.org/10.1038/s41598-022-26375-9.
Khan Munna, & Sondhi Savita. 2015. Acoustic analysis of speech under stress. International Journal of Bioinformatics Research and Applications, 11(4), 417-429.
Kumar, S., & Thiruvenkadam, S. 2021. An Analysis of the Impact of Spectral contrast Feature in Speech Emotion Recognition. 1-9. Anna University.
Lake, D. 2004. Stress: How to Cope with Pressure. The Singapore Women’s Weekly Health Series. Singapore.
Lazzuardhy, D. A., & Prasetio, B. H. 2022. Sistem Pengenalan Intensitas Emosi Sedih melalui Ucapan menggunakan Ekstraksi Bark-Frequency Cepstral Coefficient dan K-Nearest Neighbor berbasis Raspberry Pi 4. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 6(11), 1-9. Universitas Brawijaya.
Liu, J. and Yu, J., 2011. Research on development of android applications. In: Proceedings - 2011 4th International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2011. pp.69–72. https://doi.org/10.1109/ICINIS.2011.40
Loizou, C. P. 2020. Voice signal analysis techniques for cognitive decline (stress) assessment. Journal of Physics: Conference Series, 1687(1), 012007. doi:10.1088/1742-6596/1687/1/012007
Mentari, A. Z. B., & Liana, E. 2020. Teknik Manajemen Stres yang Paling Efektif pada Remaja: Literature Review. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 12(4), 1-6. https://doi.org/10.52022/jikm.v12i4.69. UPNV Jakarta.
Sigmund, M. (2013). Information Technology and Control. Statistical Analysis of Fundamental Frequency Based Features in Speech under Stress, 42(13), 3895. http://dx.doi.org/10.5755/j01.itc.42.3.3895
Pervaiz, M., & Ahmed, S. 2016. Emotion Recognition from Speech using Prosodic and Linguistic Features. Retrieved from https://www.researchgate.net/publication/309624815_Emotion_Recognition_from_Speech_using_Prosodic_and_Linguistic_Features
Subbarao, V., & Vishnu, S. 2021. Speech Emotion Recognition Using K-Nearest Neighbor Classifiers. Dalam Recent Advances in Artificial Intelligence and Data Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-3342-3_10.
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