Sistem Deteksi Penyakit Parkinson Melalui Speech Pattern Menggunakan Fitur Speech Rate, Pause Duration dan Mean Energy
Kata Kunci:
Gangguan suara, Deteksi dini, Parkinson, Analisis suara, Pemantauan kesehatanAbstrak
Pengembangan metode deteksi gangguan suara menggunakan Speech Rate, Pause Duration, dan Mean Energy dengan memanfaatkan Convolutional Neural Network (CNN). Gangguan suara dapat signifikan mempengaruhi komunikasi dan kualitas hidup seseorang. Metode ini mengintegrasikan teknologi CNN untuk mengklasifikasikan suara sebagai normal atau terganggu berdasarkan berbagai fitur akustik yang telah terbukti efektif dalam analisis suara. Penelitian ini bertujuan untuk menerapkan dan mengevaluasi akurasi sistem deteksi berbasis Speech Rate, Pause Duration, dan Mean Energy dalam menganalisis pola suara. Metodologi penelitian mencakup tahapan implementasi pra-pemrosesan sinyal suara, ekstraksi fitur, dan penggunaan perangkat keras seperti Raspberry Pi 4 Model B untuk implementasi dan pengujian sistem secara portable. Evaluasi hasil pengujian menunjukkan bahwa sistem mencapai akurasi sebesar 97% dalam mengklasifikasikan suara sebagai Parkinson atau non-Parkinson, menunjukkan potensi besar aplikasi dalam deteksi dini gangguan suara untuk pemantauan kesehatan jarak jauh.
Referensi
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Ahmed, M.N., 2022. A DEEP LEARNING APPROACH USING CONVOLUTION NEURAL NETWORK TO CLASSIFY PARKISON ’ S DISEASE. 10(6), pp.426–431.
Alissa, M., Lones, M.A., Cosgrove, J., Alty, J.E., Jamieson, S., Smith, S.L. and Vallejo, M., 2022. Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks. Neural Computing and Applications, [online] 34(2), pp.1433–1453. https://doi.org/10.1007/s00521-021-06469-7.
Anon. 2024. Empowering Internet Management : A User- Friendly Website Blocking Tool with Cross-. (January).
Arushi, Dillon, R., Teoh, A.N. and Dillon, D., 2022. Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review. [online] pp.1–41. Available at: <http://arxiv.org/abs/2208.01041>.
Bauer, M. and Garland, M., 2019. Legate NumPy: Accelerated and distributed array computing. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. https://doi.org/10.1145/3295500.3356175.
Butenko, Y., Ćosić, M., Nechaevskiy, A., Podgainy, D., Rahmonov, I., Stadnik, A., Streltsova, O. and Zuev, M., 2022. ML/DL/HPC Ecosystem of the HybriLIT Heterogeneous Platform (MLIT JINR): New Opportunities for Applied Research. Proceedings of Science, 429(July), pp.0–8. https://doi.org/10.22323/1.429.0027.
Chicho, B.T. and Sallow, A.B., 2021. A Comprehensive Survey of Deep Learning Models Based on Keras Framework. Journal of Soft Computing and Data Mining, 2(2), pp.49–62. https://doi.org/10.30880/jscdm.2021.02.02.005.
Durelli, R.S., Durelli, V.H.S., Bettio, R.W., Dias, D.R.C. and Goldman, A., 2022. Divinator: A Visual Studio Code Extension to Source Code Summarization. Proceedings of 10th Workshop on Software Visualization, Maintenance and Evolution (VEM’22), Association for Computing Machinery. https://doi.org/10.5753/vem.2022.226187.
Faragó, P., Ștefănigă, S.A., Cordoș, C.G., Mihăilă, L.I., Hintea, S., Peștean, A.S., Beyer, M., Perju-Dumbravă, L. and Ileșan, R.R., 2023. CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments. Bioengineering, 10(5). https://doi.org/10.3390/bioengineering10050531.
Fernando, E.:, Rincon, A., Carrera, V. and De Empresas, A., n.d. Técnicas Y Tecnologías Para La Compra Inteligente En Una Empresa Del Sector Telecomunicaciones (Ifx Networks) Intelligent Purchasing Techniques and Technologies in a Telecommunications Sector Company. p.2022.
Giannakopoulos, T., 2015. PyAudioAnalysis: An open-source python library for audio signal analysis. PLoS ONE, 10(12), pp.1–17. https://doi.org/10.1371/journal.pone.0144610.
Igras-Cybulska, M., Ziółko, B., Żelasko, P. and Witkowski, M., 2016. Structure of pauses in speech in the context of speaker verification and classification of speech type. Eurasip Journal on Audio, Speech, and Music Processing, 2016(1). https://doi.org/10.1186/s13636-016-0096-7.
Jegan, R. and R, J., 2020. Voice Disorder Detection And Classification- A Review. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3734762.
Liu, S., Nakajima, Y., Chen, L., Arndt, S., Kakizoe, M., Elliott, M.A. and Remijn, G.B., 2022. How Pause Duration Influences Impressions of English Speech: Comparison Between Native and Non-native Speakers. Frontiers in Psychology, 13(February), pp.1–14. https://doi.org/10.3389/fpsyg.2022.778018.
Mahr, T.J., Rathouz, P.J., Soriano, J.U. and Hustad, K.C., 2021. Speech development between 30 and 119 months in typical children ii: Articulation rate growth curves. Journal of Speech, Language, and Hearing Research, 64(11), pp.4057–4070. https://doi.org/10.1044/2021_JSLHR-21-00206.
Malek, A., 2023. Spafe: Simplified python audio features extraction. Journal of Open Source Software, 8(81), p.4739. https://doi.org/10.21105/joss.04739.
Mohammed, M.A., Abdulkareem, K.H., Mostafa, S.A., Ghani, M.K.A., Maashi, M.S., Garcia-Zapirain, B., Oleagordia, I., Alhakami, H. and Al-Dhief, F.T., 2020. Voice pathology detection and classification using convolutional neural network model. Applied Sciences (Switzerland), 10(11), pp.1–13. https://doi.org/10.3390/app10113723.
Novela, M. and T. Basaruddin, 2021. Dataset Suara dan Teks Berbahasa Indonesia Pada Rekaman Podcast dan Talk show. Jurnal Fasilkom, 11(2), pp.61–66. https://doi.org/10.37859/jf.v11i2.2628.
Peta, S., 2022. Python- An Appetite for the Software Industry. International Journal of Programming Languages and Applications, 12(4), pp.1–14. https://doi.org/10.5121/ijpla.2022.12401.
Purwins, H., Li, B., Virtanen, T., Schlüter, J., Chang, S.Y. and Sainath, T., 2019. Deep Learning for Audio Signal Processing. IEEE Journal on Selected Topics in Signal Processing, 13(2), pp.206–219. https://doi.org/10.1109/JSTSP.2019.2908700.
Saeed, U., Khuhro, M.A., Waqas, M. and Mirbahar, N., 2022. Comparative analysis of different Operating systems for Raspberry Pi in terms of scheduling, synchronization, and memory management. Mehran University Research Journal of Engineering and Technology, 41(3), pp.113–119. https://doi.org/10.22581/muet1982.2203.11.
Syed, M.A., 2020. Overview on Open Source Machine Learning Platforms-TensorFlow. SSRN Electronic Journal, 2020(11), pp.11–14. https://doi.org/10.2139/ssrn.3732837.
Vasil, J., Badcock, P.B., Constant, A., Friston, K. and Ramstead, M.J.D., 2020. A World Unto Itself: Human Communication as Active Inference. Frontiers in Psychology, 11(March), pp.1–26. https://doi.org/10.3389/fpsyg.2020.00417.
Verma, V., Benjwal, A., Chhabra, A., Singh, S.K., Kumar, S., Gupta, B.B., Arya, V. and Chui, K.T., 2023. A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection. Scientific Reports, [online] 13(1), pp.1–17. https://doi.org/10.1038/s41598-023-49869-6.
Wang, X., Qi, L., Yang, H., Rao, Y. and Chen, H., 2023. Stretchable synaptic transistors based on the field effect for flexible neuromorphic electronics. Soft Science, 3(2). https://doi.org/10.20517/ss.2023.06.
Xu, T., Han, B. and Qian, F., 2019. Analyzing viewport prediction under different VR interactions. CoNEXT 2019 - Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies, pp.165–171. https://doi.org/10.1145/3359989.3365413.
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