Klasifikasi Rentang Usia Dan Gender Dengan Deteksi Suara Menggunakan Metode Deep Learning Algoritma CNN (Convolutional Neural Network)
DOI:
https://doi.org/10.34010/komputika.v12i2.10516Abstract
ABSTRACT – In this research, we delve into the identification of human voices based kata.on gender by leveraging the differences in vocal characteristics between males and females. In addition to differences in vocal tract size, factors such as length, thickness, and vocal cord stiffness also play a role in producing differences in the fundamental frequency of voice between the two genders. The fundamental frequency of voice becomes one of the indicators used in acoustic analysis for gender classification based on voice. In the automatic classification of voices, sound processing techniques and machine learning are pivotal in system development. The method of gender recognition based on voice involves acoustic analysis using voice features such as fundamental frequency, formants, duration, intensity, and intonation patterns. The research yielded an accuracy of 92% through modeling using CNN on audio data, and the testing results were quite satisfactory in terms of classification. This model's results have been implemented into a Flask API, serving as a connection or backend for an application. The application takes the form of a movie recommendation system developed using the Flutter framework. Consequently, within the movie application, there is voice clustering or classification of user voices to provide film recommendations within the application
Keywords – Deep Learning, Voice Recognition, Audio Classification, CNN, Gender
References
A. A. Shafhah, P. P. Adikara, and S. Adinugroho, “Klasifikasi Jenis Kelamin Berdasarkan Suara Menggunakan Metode Learning Vector Quantization,” vol. 4, no. 7, pp. 2301–2308, 2020, [Online]. Available: http://j-ptiik.ub.ac.id
A. Mathematics, “済無No Title No Title No Title,” vol. 2, no. 1, pp. 1–23, 2016.
A. A. Alwi, P. P. Adikara, and Indriati, “Pengenalan Jenis Kelamin dan Rentang Umur berdasarkan Suara menggunakan Metode Backpropagation Neural Network,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 7, pp. 2083–2093, 2020.
R. B. Handoko and S. Suyanto, “Klasifikasi Gender Berdasarkan Suara Menggunakan Support Vector Machine,” Indones. J. Comput., vol. 4, no. 1, p. 9, 2019, doi: 10.21108/indojc.2019.4.1.244.
I. S. Pratama and F. I. Kurniadi, “Klasifikasi Jenis Kelamin Berdasarkan Pitch Suara Menggunakan Metode Pitch Detection Algorithm,” J. Sist. Komput. dan Kecerdasan Buatan, vol. II, no. 1, 2018.
S. Bagas Bhaskoro and A. Riedho, “Aplikasi Pengenalan Gender Menggunakan Suara,” Semin. Nas. Apl. Teknol. Inf., vol. 2012, no. Snati, pp. 15–16, 2012.
F. D. Adhinata, D. P. Rakhmadani, and A. J. T. Segara, “Pengenalan Jenis Kelamin Manusia Berbasis Suara Menggunakan MFCC dan GMM,” J. Dinda Data Sci. Inf. Technol. Data Anal., vol. 1, no. 1, pp. 28–33, 2021, doi: 10.20895/dinda.v1i1.198.
I. A. Sabilla, “Arsitektur Convolutional Neural Network (Cnn) Untuk Klasifikasi Jenis Dan Kesegaran Buah Pada Neraca Buah,” Tesis, no. 201510370311144, pp. 1–119, 2020, [Online]. Available: https://repository.its.ac.id/73567/1/05111850010020-Master_Thesis.pdf
S. Prihatiningsih, N. S. M, F. Andriani, and N. Nugraha, “Analisa Performa Pengenalan Tulisan Tangan Angka Berdasarkan Jumlah Iterasi Menggunakan Metode Convolutional Neural Network,” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 1, pp. 58–66, 2019, doi: 10.35760/tr.2019.v24i1.1934.
S. Lasniari, J. Jasril, S. Sanjaya, F. Yanto, and M. Affandes, “Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi,” J. Nas. Komputasi dan Teknol. Inf., vol. 5, no. 3, pp. 474–481, 2022, doi: 10.32672/jnkti.v5i3.4424.
A. F. H. Dhiya Mahdi Asriny, Septia Rani, “Implementasi Deep Learning Menggunakan Convolutional Neural Network Untuk Klasifikasi Citra Mikroskopis Stomata Tanaman Herbal Curcuma,” pp. 1–5, 2019, [Online]. Available: https://repositori.usu.ac.id/handle/123456789/24599
I. Wulandari, H. Yasin, and T. Widiharih, “Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (Cnn),” J. Gaussian, vol. 9, no. 3, pp. 273–282, 2020, doi: 10.14710/j.gauss.v9i3.27416.
A. Peryanto, A. Yudhana, and R. Umar, “Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network,” Format J. Ilm. Tek. Inform., vol. 8, no. 2, p. 138, 2020, doi: 10.22441/format.2019.v8.i2.007.
T. Bariyah, M. A. Rasyidi, and N. Ngatini, “Convolutional Neural Network untuk Metode Klasifikasi Multi-Label pada Motif Batik,” Techno.Com, vol. 20, no. 1, pp. 155–165, 2021, doi: 10.33633/tc.v20i1.4224.
T. Dwi Antoko, M. Azhar Ridani, and A. Eko Minarno, “Klasifikasi Buah Zaitun Menggunakan Convolution Neural Network,” Komputika J. Sist. Komput., vol. 10, no. 2, pp. 119–126, 2021, doi: 10.34010/komputika.v10i2.4475.
