Implementasi Deep Learning dalam Pengklasifikasian Wajah Menggunakan Library Tensorflow pada Algoritma Convolutional Neural Network (CNN)

Rahmat Setiawan Usman, Isran K. Hasan, Dewi Rahmawaty Isa

Abstract


The convolutional neural network is a deep learning method that functions to recognize and classify objects in an image. An example of its application is a facial recognition system which consists of a detection and classification process. Facial recognition by computers can be influenced by many things such as lighting, expressions, and the amount of dataset provided. This study aims to find out how to implement CNN to identify faces using Tensorflow with the Python programming language. The number of datasets used is 120 data and 10 respondents in total with different lighting conditions and shooting angles. Apart from the dataset, this study also uses several different scenarios in the training process, namely the difference in the number of epochs and the difference in the number of learning rates. Based on the results of the discussion, two models were obtained. In the first model, the results obtained an accuracy of 100% in the training process and 65% in the testing process. In the second model, the results obtained are 100% accuracy in the training process and 75% in the testing process. performance of the model made in this study can be said to be optimal in recognizing objects in several lighting conditions and image angles.

Keywords


Deep learning, Convolutional neural network (CNN), Tensorflow, Image classification

Full Text:

PDF

References


Anggraini, W. (2020) Deep Learning Untuk Deteksi Wajah Yang Berhijab Menggunakan Algoritma Convolutional Neural Network (CNN) Dengan Tensorflow. UNIVERSITAS ISLAM NEGERI AR-RANIRY.

Goodfellow, I., Bengio, Y. and Courville, A. (2016) Deep Learning. USA: MIT PRESS.

Ketkar, N. (2017) Deep Learning with Python. Berkeley, CA: Apress. doi: 10.1007/978-1-4842-2766-4.

Kumar, A., Kaur, A. and Kumar, M. (2019) ‘Face detection techniques: a review’, Artificial Intelligence Review, 52(2), pp. 927–948. doi: 10.1007/s10462-018-9650-2.

Nadira, M. (2019) Implementasi Deep Learning dengan Metode Convolutional Neural Network untuk Identifikasi Citra Bahan Kulit Hewan. UNIVERSITAS PEMBANGUNAN NASIONAL “VETERAN” JAKARTA.

Nengsih, W. (2020) ‘CNN Modelling Untuk Deteksi Wajah Berbasis Gender Menggunakan Python’, Jurnal Komputer Terapan, 6(2), pp. 190–199. doi: 10.35143/jkt.v6i2.3679.

Nurfita, R. D. and Ariyanto, G. (2018) ‘Implementasi Deep Learning berbasis Tensorflow untuk Pengenalan Sidik Jari’, Emitor: Jurnal Teknik Elektro, 18(1).

O’Shea, K. and Nash, R. (2015) ‘An Introduction to Convolutional Neural Networks’, CoRR, abs/1511.0.

Qi, X., Wang, T. and Liu, J. (2017) ‘Comparison of Support Vector Machine and Softmax Classifiers in Computer Vision’, in 2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE). IEEE, pp. 151–155. doi: 10.1109/ICMCCE.2017.49.

Tumuli, A. D. L., Najoan, X. B. N. and Sambul, A. (2017) ‘Implementasi Teknologi Biometrical Identification untuk Login Hotspot’, Jurnal Teknik Informatika, 12(1). doi: 10.35793/jti.12.1.2017.17873.

Zufar, M. (2016) Convolutional Neural Networks Untuk Pengenalan Wajah Secara Real-Time. Institut Teknologi Sepuluh Nopember.




DOI: https://doi.org/10.37905/jjps.v4i2.18264

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Jambura Journal of Probability and Statistics

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Editorial Office of Jambura Journal of Probability and Statistics:
 
Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
Telp: +6285398740008 (Call/SMS/WA)
E-mail: redaksi.jjps@ung.ac.id