PENERAPAN HAIR RECOGNITION MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER DAN CNN DEEP LEARNING
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Abstract
Zaman modernisasi seperti saat ini, proses identifikasi berkembang sangat pesat dan banyak diterapkan di berbagai aplikasi. Teknologi pengenalan rambut menggunakan Artificial Intelligence untuk mengenali rambut seseorang. Penelitian ini menggunakan pengenalan rambut real-time berbasis OpenCV dan algoritma histogram pola biner lokal dan metode Haar Cascade classifier untuk mengklasifikasikan jenis-jenis rambut yang ada. Sistem disini dapat mendeteksi, selain itu mengenali serta serta membandingkan rambut yang ditangkap kamera yang ada didalam database rambut yang sudah tersimpan. Citra rambut yang digunakan adalah citra RGB dengan ukuran 480 x 680 piksel dan berekstensi .jpg atau .png yang dapat diubah kedalam citra grayscale agar dapat melakukan pengkasifikasian jenis rambut. Dengan memodelkan sistem seperti ini diharapkan dapat membantu masyarakat dalam mengklasifikasikan jenis-jenis rambut.
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References
[2] ACADEIC JOURNELA.INC, “Research JOURNAL OF INFORMATION TECHNOLOGY,” Comp. Anal. Illum. Compens. Tech. FACE Recognit., 2015.
[3] B. Tryatmojo and R. I. S. Maryati, “Akurasi Sistem Face Recognition OpenCV Menggunakan Raspberry Pi Dengan Metode Haar Cascade,” J. Ilm. Inform., vol. 7, no. 2, pp. 92–98, 2019.
[4] I. S. Pratama, Muhammad Rizky; Rizal, Achmad; Sumaryo, “Desain Sistem Deteksi Objek Real Time Dengan Metode Haar Cascade Classifier Real Time Object Detection System Design Using Haar Cascade Classifier Method,” vol. 7, no. 1, pp. 26–34, 2020.
[5] C. E. Panjaitan, D. Hagayna, D. Prandi, R. Wiranto, P. T. Elektro, and F. Teknologi, “JITE ( Journal of Informatics and Telecommunication Engineering ) Integration Face Recognition and Body Temperature,” vol. 5, no. July, 2021.
[6] A. Nurhopipah and A. Harjoko, “Motion Detection and Face Recognition for CCTV Surveillance System,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 12, no. 2, p. 107, 2018, doi: 10.22146/ijccs.18198.
[7] E. N. Arrofiqoh and H. Harintaka, “Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Tanaman Pada Citra Resolusi Tinggi,” Geomatika, vol. 24, no. 2, p. 61, 2018, doi: 10.24895/jig.2018.24-2.810.
[8] K. Nguyen, C. Fookes, A. Ross, and S. Sridharan, “Iris Recognition with Off-the-Shelf CNN Features: A Deep Learning Perspective,” IEEE Access, vol. 6, pp. 18848–18855, 2017, doi: 10.1109/ACCESS.2017.2784352.
[9] W. S. Eka Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016, doi: 10.12962/j23373539.v5i1.15696.
[10] V. Bhatia, “The Three Hair Types,” Kaggle, 2020. https://www.kaggle.com/vyombhatia/the-three-hair-types?select=Wavy+Hair (accessed Jan. 04, 2022).