Sistem Pengenalan Wajah pada Keamanan Ruangan Berbasis Convolutional Neural Network

S Sunardi(1*), Abdul Fadlil(2), Denis Prayogi(3),

(1) Universitas Ahmad Dahlan
(2) Universitas Ahmad Dahlan
(3) Universitas Ahmad Dahlan
(*) Corresponding Author

Abstract


Face recognition is a biometric system that is widely applied in various fields especially in the security for identify and verify purposes. For every method of face recognition, they have a unique ways on the process with their advantages and disadvantages themselves. This study designs a face recognition system that is applied to a room security system using the Convolutional Neural Network (CNN). This method works by imitating the way nerve cells to communicate with interrelated neurons or rather mimics how artificial neural networks work in humans. The process of taking images as training data and the face recognition process using a webcam camera installed on a Raspberry pi-based device and python programming language with tensorflow library. Based on the results of research obtained using 875 data samples which were divided into 75% for training and 25% (or 219 data) for testing data produce predictions with 100% accuracy that means all data were successfully recognized.

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References


N. Heryana, Rini Mayasari, and Kiki Ahmad Baihaqi, “Penerapan Haar Cascade Classification Model Untuk Deteksi Wajah, Hidung, Mulut, dan Mata Menggunakan Algoritma Viola-Jones,” Techno Xplore J. Ilmu Komput. dan Teknol. Inf., vol. 5, no. 1, pp. 21–25, 2020, doi: 10.36805/technoxplore.v5i1.1064.

S. Yulina, “Penerapan Haar Cascade Classifier dalam Mendeteksi Wajah dan Transformasi Citra Grayscale Menggunakan OpenCV,” J. Politek. Caltex Riau, vol. 7, no. 1, pp. 100–109, 2021.

W. Anggraini, “Deep Learning untuk Deteksi Wajah yang Berhijab Menggunakan Algoritma Convolutional Neural Network (Cnn) dengan Tensorflow,” Universitas Islam Negeri Ar-Raniry Darussalam, 2020.

V. D. WIN, “Pengenalan Wajah Menggunakan Convolutional Neural Network,” Institut Teknologi Sepuluh Nopember, 2018.

S. Al-Aidid and D. Pamungkas, “Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram,” J. Rekayasa Elektr., vol. 14, no. 1, pp. 62–67, 2018, doi: 10.17529/jre.v14i1.9799.

R. Purwati and G. Ariyanto, “Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern,” Emit. J. Tek. Elektro, vol. 17, no. 2, pp. 29–38, 2017, doi: 10.23917/emitor.v17i2.6232.

A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 45–51, 2020, doi: 10.30871/jaic.v4i1.2017.

S. K. Wildah, S. Agustiani, A. Mustopa, N. Wuryani, M. Nawawi, and R. A. Safitri, “Pengenalan Wajah Menggunakan Pembelajaran Mesin Berdasarkan Ekstraksi Fitur pada Gambar Wajah Berkualitas Rendah Face Recognition Using Machine Learning Based on Feature Extraction on Low Quality Face Images,” INFOTECH J. Inform. Teknol., vol. 2, no. 2, pp. 95–103, 2021.

Sriyati, A. Setyanto, and E. T. Luthfi, “Literature Review: Pengenalan Wajah Menggunakan Algoritma Convolutional Neural Network,” J. Teknol. Inf. dan Komun., vol. 8, no. 2, 2020, doi: 10.30646/tikomsin.v8i2.463.

A. Sukusvieri, “Implementasi Metode Single Shot Detector untuk Pengenalan Wajah,” Universitas Dinamika, 2020.

P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network ( Cnn ) Pada Ekspresi Manusia,” Algor, vol. 2, no. 1, pp. 12–21, 2020.

Y. R. W. Utami and T. Susyanto, “Unjuk Kerja Metode Klasifikasi Support Vector Machine (SVM) dengan Learning Vector Quantization (LVQ) pada Aplikasi Pengenalan Wajah,” J. Ilm. SINUS, pp. 9–18, 2012.

S. Abidin, “Deteksi Wajah Menggunakan Metode Haar Cascade Classifier Berbasis Webcam Pada Matlab,” J. Teknol. Elekterika, vol. 15, no. 1, p. 21, 2018, doi: 10.31963/elekterika.v15i1.2102.

A. R. Syafira and G. Ariyanto, “Sistem Deteksi Wajah Dengan Modifikasi Metode Viola Jones,” Emit. J. Tek. Elektro, vol. 17, no. 1, pp. 26–33, 2017, doi: 10.23917/emitor.v17i1.5964.

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.




DOI: http://dx.doi.org/10.30645/j-sakti.v6i2.480

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