Sistem Deteksi Depresi melalui Pengenalan Pola Suara dengan Mengimplementasikan Metode Prosody Analysis
Kata Kunci:
Depresi, CNN, Raspberry Pi 4, Prosody AnalysisAbstrak
Depresi merupakan jenis gangguan mental yang cukup umum dijumpai, umumnya depresi akan muncul pada remaja umur 13 – 15 tahun, dan akan mencapai puncaknya pada umur 17 – 18 tahun. Menurut data Riset Kesehatan Dasar (RISKESDAS) pada tahun 2018, penduduk indonesia dengan umur diatas 15 tahun tercatat sebanyak 706.689 terkena gangguan mental depresi. Lebih lanjut, data juga menunjukkan bahwa setidaknya sekitar 1.800 orang melakukan bunuh diri setiap tahunnya akibat depresi. Penelitian ini bertujuan untuk membuat alat yang dapat mendeteksi gangguan mental depresi dengan efisien dan dapat digunakan dimana saja. Alat tersebut dirancang menggunakan metode prosody analysis dengan menerapkan ekstraksi fitur yang dimiliki oleh prosody analysis. Alat akan diimplementasikan ke dalam Raspberry Pi 4 Model B. Penelitian ini menggunakan dataset yang telah disesuaikan dengan durasi 3 detik pada setiap file. Algoritma CNN (Convolution Neural Network) akan digunakan untuk tahap klasifikasi, apabila data yang ada telah melalui ekstraksi fitur. Alat akan dioperasikan menggunakan LCD Display dengan ukuran 3.5 inci dan GUI (Graphical User Interface) akan digunakan sebagai layar utama pada LCD. Hasil penelitian yang telah dilakukan menunjukkan bahwa algoritma CNN memiliki tingkat akurasi sebesar 97%, sementara alat mendapatkan tingkat ketepatan sebesar 80% dari 15 data uji dengan 12 data diantara dapat di prediksi dengan tepat, depresi mendapatkan tingkat ketepatan sebesar 71% dan non-depresi mendapatkan tingkat ketepatan sebesar 87%. Dari penelitian yang telah dilaksanakan, alat serta sistem yang telah dirancang dapat bekerja dengan baik sehingga dapat dikembangkan lebih lanjut.
Kata kunci: Prosody Analysis, Depresi, CNN, Raspberry Pi 4
Referensi
Albawi, S. and Mohammed, T.A., 2017. Understanding of a Convolutional Neural Network. 2017 International Conference on Engineering and Technology (ICET), pp.1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186.
Anggraini, N.A. and Fadillah, N., 2019. Analisis Deteksi Emosi Manusia dari Suara Percakapan Menggunakan Matlab dengan Metode KNN. InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan), 3(2), pp.176–179. https://doi.org/10.30743/infotekjar.v3i2.1041.
Anjani, D.A.V.N., Chauna, S., Almuwaffaq, H.W., Apryani, R. and Amalia, E., 2023. Perkembangan Terkini Perilaku Bunuh Diri pada Orang dengan Depresi. Unram Medical Journal, 12(2), pp.180–189. https://doi.org/10.29303/jk.v12i2.4394.
Badan Penelitian Dan Pengembangan Kesehatan Republik Indonesia, 2018. Laporan Riskesdas 2018 Nasional.pdf. Lembaga Penerbit Balitbangkes, .
Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J. and Quatieri, T.F., 2015. A review of depression and suicide risk assessment using speech analysis. Speech Communication, [online] 71, pp.10–49. https://doi.org/10.1016/j.specom.2015.03.004.
Dahmani, M. and Guerti, M., 2020. Recurrence quantification analysis of glottal signal as non linear tool for pathological voice assessment and classification. International Arab Journal of Information Technology, 17(6), pp.857–866. https://doi.org/10.34028/iajit/17/6/4.
Dwi, R., Rahmasari, P. and Af, N., 2024. Simulasi Penghilangan Noise pada Sinyal Suara menggunakan Metode Fast Fourier Transfrom ( Simulation of Noise Removal in Sound Signals by Using Fast Fourier Transform Method ). (1), pp.1–7.
Elhakeem, E.S., Elmaghraby, R.M. and Ibrahim, S.M., 2023. Objective measurement of prosodic features of speech of hearing-impaired Egyptian children with cochlear implants. Egyptian Journal of Otolaryngology, [online] 39(1). https://doi.org/10.1186/s43163-023-00400-8.
Hema, C. and Garcia Marquez, F.P., 2023. Emotional speech Recognition using CNN and Deep learning techniques. Applied Acoustics, [online] 211, p.109492. https://doi.org/10.1016/j.apacoust.2023.109492.
Jia, Y., Liang, Y. and Zhu, T., 2019. An analysis of voice quality of chinese patients with depression. 2019 22nd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2019, 2019-Janua(October 2019). https://doi.org/10.1109/O-COCOSDA46868.2019.9060848.
Kenfack Jeuguim, M.S., Melatagia Yonta, P. and Sandembouo, E., 2024. YembaTones: A syllable-tone annotated dataset for speech recognition and prosodic analysis of the Yemba language. Data in Brief, [online] 52, p.109860. https://doi.org/10.1016/j.dib.2023.109860.
Mobile, J., 2019. Ucapan Menggunakan Linear Predictor Ceptral. 1(2), pp.48–56.
Nanni, L., Maguolo, G., Brahnam, S. and Paci, M., 2021. An ensemble of convolutional neural networks for audio classification. Applied Sciences (Switzerland), 11(13). https://doi.org/10.3390/app11135796.
Pervaiz, M. and Ahmed, T., 2016. Emotion Recognition from Speech using Prosodic and Linguistic Features. International Journal of Advanced Computer Science and Applications, 7(8), pp.84–90. https://doi.org/10.14569/ijacsa.2016.070813.
Pramanick, D., Ansar, H., Kumar, H., Pranav, S., Tengshe, R. and Fatimah, B., 2021. Deep learning based urban sound classification and ambulance siren detector using spectrogram. 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, pp.1–6. https://doi.org/10.1109/ICCCNT51525.2021.9579778.
Robert Cronin Yung Peng, Rose Khavari, N.D., 2017. 乳鼠心肌提取 HHS Public Access. Physiology & behavior, 176(3), pp.139–148. https://doi.org/10.1159/000444169.Carotid.
Rois, H., 2021. Prosogram : Kolaborasi dan Otomatisasi Prosodi pada Penutur Pandhalungan. Prosiding Seminar Nasional Linguistik dan Sastra (Semantiks), [online] pp.450–460. Available at: <https://jurnal.uns.ac.id/prosidingsemantiks/article/view/53052>.
Setiawati, E. and Widagdo, T.B., 2021. Strategi Kesantunan Tindak Tutur Direktif Werkudara Dalam Wayang Purwa: Analisis Pola Prosodi. Litera, 20(1), pp.154–178. https://doi.org/10.21831/ltr.v20i1.34058.
Suartika E. P, I.W., 2016. Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101. Jurnal Teknik ITS, [online] 5(1), p.76. Available at: <http://repository.its.ac.id/48842/>.
Taye, M.M., 2023. Theoretical Understanding of Convolutional Neural Network : Mdpi Ag, 11(3), p.52.
Tuasikal, A.N.A. and Retnowati, S., 2019. Kematangan Emosi, Problem-Focused Coping, Emotion-Focused Coping dan Kecenderungan Depresi pada Mahasiswa Tahun Pertama. Gadjah Mada Journal of Psychology (GamaJoP), 4(2), p.105. https://doi.org/10.22146/gamajop.46356.
Wahyuningtyas, V., 2021. Implementasi Ekstraksi Fitur untuk Klasifikasi Suara Urban Menggunakan Deep Learning. Sains, Aplikasi, Komputasi dan Teknologi Informasi, 3(1), pp.10–17.
Wantini, A.A., 2019. the Validity Test of Depression Screening Instrument in Adolescences. Jurnal Berkala Epidemiologi, 7(2), p.155. https://doi.org/10.20473/jbe.v7i22019.155-162.
Xing, Y., Liu, Z., Chen, Q., Li, G., Ding, Z., Feng, L. and Hu, B., 2023. Depression recognition base on acoustic speech model of Multi-task emotional stimulus. Biomedical Signal Processing and Control, [online] 85(April), p.104970. https://doi.org/10.1016/j.bspc.2023.104970.
Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng and Jun Zhou, 2021. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12), pp.6999–7019
Albawi, S. and Mohammed, T.A., 2017. Understanding of a Convolutional Neural Network. 2017 International Conference on Engineering and Technology (ICET), pp.1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186.
Anggraini, N.A. and Fadillah, N., 2019. Analisis Deteksi Emosi Manusia dari Suara Percakapan Menggunakan Matlab dengan Metode KNN. InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan), 3(2), pp.176–179. https://doi.org/10.30743/infotekjar.v3i2.1041.
Anjani, D.A.V.N., Chauna, S., Almuwaffaq, H.W., Apryani, R. and Amalia, E., 2023. Perkembangan Terkini Perilaku Bunuh Diri pada Orang dengan Depresi. Unram Medical Journal, 12(2), pp.180–189. https://doi.org/10.29303/jk.v12i2.4394.
Badan Penelitian Dan Pengembangan Kesehatan Republik Indonesia, 2018. Laporan Riskesdas 2018 Nasional.pdf. Lembaga Penerbit Balitbangkes, .
Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J. and Quatieri, T.F., 2015. A review of depression and suicide risk assessment using speech analysis. Speech Communication, [online] 71, pp.10–49. https://doi.org/10.1016/j.specom.2015.03.004.
Dahmani, M. and Guerti, M., 2020. Recurrence quantification analysis of glottal signal as non linear tool for pathological voice assessment and classification. International Arab Journal of Information Technology, 17(6), pp.857–866. https://doi.org/10.34028/iajit/17/6/4.
Dwi, R., Rahmasari, P. and Af, N., 2024. Simulasi Penghilangan Noise pada Sinyal Suara menggunakan Metode Fast Fourier Transfrom ( Simulation of Noise Removal in Sound Signals by Using Fast Fourier Transform Method ). (1), pp.1–7.
Elhakeem, E.S., Elmaghraby, R.M. and Ibrahim, S.M., 2023. Objective measurement of prosodic features of speech of hearing-impaired Egyptian children with cochlear implants. Egyptian Journal of Otolaryngology, [online] 39(1). https://doi.org/10.1186/s43163-023-00400-8.
Hema, C. and Garcia Marquez, F.P., 2023. Emotional speech Recognition using CNN and Deep learning techniques. Applied Acoustics, [online] 211, p.109492. https://doi.org/10.1016/j.apacoust.2023.109492.
Jia, Y., Liang, Y. and Zhu, T., 2019. An analysis of voice quality of chinese patients with depression. 2019 22nd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2019, 2019-Janua(October 2019). https://doi.org/10.1109/O-COCOSDA46868.2019.9060848.
Kenfack Jeuguim, M.S., Melatagia Yonta, P. and Sandembouo, E., 2024. YembaTones: A syllable-tone annotated dataset for speech recognition and prosodic analysis of the Yemba language. Data in Brief, [online] 52, p.109860. https://doi.org/10.1016/j.dib.2023.109860.
Mobile, J., 2019. Ucapan Menggunakan Linear Predictor Ceptral. 1(2), pp.48–56.
Nanni, L., Maguolo, G., Brahnam, S. and Paci, M., 2021. An ensemble of convolutional neural networks for audio classification. Applied Sciences (Switzerland), 11(13). https://doi.org/10.3390/app11135796.
Pervaiz, M. and Ahmed, T., 2016. Emotion Recognition from Speech using Prosodic and Linguistic Features. International Journal of Advanced Computer Science and Applications, 7(8), pp.84–90. https://doi.org/10.14569/ijacsa.2016.070813.
Pramanick, D., Ansar, H., Kumar, H., Pranav, S., Tengshe, R. and Fatimah, B., 2021. Deep learning based urban sound classification and ambulance siren detector using spectrogram. 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, pp.1–6. https://doi.org/10.1109/ICCCNT51525.2021.9579778.
Robert Cronin Yung Peng, Rose Khavari, N.D., 2017. 乳鼠心肌提取 HHS Public Access. Physiology & behavior, 176(3), pp.139–148. https://doi.org/10.1159/000444169.Carotid.
Rois, H., 2021. Prosogram : Kolaborasi dan Otomatisasi Prosodi pada Penutur Pandhalungan. Prosiding Seminar Nasional Linguistik dan Sastra (Semantiks), [online] pp.450–460. Available at: <https://jurnal.uns.ac.id/prosidingsemantiks/article/view/53052>.
Setiawati, E. and Widagdo, T.B., 2021. Strategi Kesantunan Tindak Tutur Direktif Werkudara Dalam Wayang Purwa: Analisis Pola Prosodi. Litera, 20(1), pp.154–178. https://doi.org/10.21831/ltr.v20i1.34058.
Suartika E. P, I.W., 2016. Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101. Jurnal Teknik ITS, [online] 5(1), p.76. Available at: <http://repository.its.ac.id/48842/>.
Taye, M.M., 2023. Theoretical Understanding of Convolutional Neural Network : Mdpi Ag, 11(3), p.52.
Tuasikal, A.N.A. and Retnowati, S., 2019. Kematangan Emosi, Problem-Focused Coping, Emotion-Focused Coping dan Kecenderungan Depresi pada Mahasiswa Tahun Pertama. Gadjah Mada Journal of Psychology (GamaJoP), 4(2), p.105. https://doi.org/10.22146/gamajop.46356.
Wahyuningtyas, V., 2021. Implementasi Ekstraksi Fitur untuk Klasifikasi Suara Urban Menggunakan Deep Learning. Sains, Aplikasi, Komputasi dan Teknologi Informasi, 3(1), pp.10–17.
Wantini, A.A., 2019. the Validity Test of Depression Screening Instrument in Adolescences. Jurnal Berkala Epidemiologi, 7(2), p.155. https://doi.org/10.20473/jbe.v7i22019.155-162.
Xing, Y., Liu, Z., Chen, Q., Li, G., Ding, Z., Feng, L. and Hu, B., 2023. Depression recognition base on acoustic speech model of Multi-task emotional stimulus. Biomedical Signal Processing and Control, [online] 85(April), p.104970. https://doi.org/10.1016/j.bspc.2023.104970.
Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng and Jun Zhou, 2021. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12), pp.6999–7019
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