Analisis Performa Algoritma Convolutional Neural Networks Menggunakan Arsitektur LeNet dan VGG16
Abstract
Identifying a person's self-identity can be done by recognizing facial images, where faces can often represent a person's identity. Facial identification with technology can benefit the effectiveness efficiency and accuracy of data. This identification process can be used with the help of algorithms that will check digital images with the necessary detection results. One algorithm that can be applied in classifying and detecting gender through facial image algorithms is Convolutional Neural Networks. Convolutional Neural Network algorithms have various architectures that have advantages in each architecture. This study compared the process of identifying a person's face to obtain information in the form of gender. The models compared in this study are the LeNet model and the VGG16 model. The identification and detection process was carried out using 800 photos for data training with gender labeling data and 240 photos for testing data. A comparison of these two models is necessary to get the best final model result. The final results obtained from this study the best accuracy of both architectures was obtained in the VGG16 architecture which reached an average accuracy of 100 in several epochs compared to the VGG16 architecture at 0.925 in the 46th epoch. This is due to a Rectified Linear Unit (ReLU) on the VGG16 architecture which can minimize errors and saturation.
Keywords
Full Text:
PDF (Indonesian)References
Y. Sari, Ekstraksi Fitur dan Aplikasinya pada Citra 2D. Lampung: Perahu Litera, 2022.
G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.
S. Bunrit, N. Kerdprasop, and K. Kerdprasop, “Improving the representation of cnn based features by autoencoder for a task of construction material image classification,” J. Adv. Inf. Technol., vol. 11, no. 4, pp. 192–199, 2020, doi: 10.12720/jait.11.4.192-199.
A. Sharma and G. Phonsa, “Image Classification Using CNN,” SSRN Electron. J., no. Icicc, pp. 1–5, 2021, doi: 10.2139/ssrn.3833453.
I. Amerini, C. T. Li, and R. Caldelli, “Social Network Identification Through Image Classification with CNN,” IEEE Access, vol. 7, no. c, pp. 35264–35273, 2019, doi: 10.1109/ACCESS.2019.2903876.
D. Devito, R. C. Wihandika, and A. W. Widodo, “Ekstraksi Ciri Untuk Klasifikasi Gender Berbasis Citra Wajah Menggunakan Metode Histogram of Oriented Gradients,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 8, pp. 8002–8011, 2019.
S. A. Farhan, I. J. Raharjo, and N. Kumalasari, “Identifikasi Wajah Berdasarkan Gender Dan Kelompok Usia Dengan Metode Viola Jones Dan Metode Jaringan Syaraf Tiruan Face Identification Base on Gender and Age Group With Viola Jones Method and Artificial Neuron Network Method,” Telkomuniversity, vol. 6, no. 2, p. 8, 2019.
C. E. Magdalena, H. F. Tresna, and S. Sa’idah, “Identifikasi Gender Individu Melalui Pola Rugae Palatina Berbasis Smartphone Android Gender Types of Individuals Identification Through Rugae Palatine Based on Android Smartphone,” e-Proceeding Eng., vol. 8, no. 5, pp. 4991–4999, 2021.
H. Dang, F. Liu, J. Stehouwer, X. Liu, and A. K. Jain, “On the Detection of Digital Face Manipulation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 5780–5789, 2020, doi: 10.1109/CVPR42600.2020.00582.
A. J. Larrazabal, N. Nieto, V. Peterson, D. H. Milone, and E. Ferrante, “Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis,” Proc. Natl. Acad. Sci. U. S. A., vol. 117, no. 23, pp. 12592–12594, 2020, doi: 10.1073/pnas.1919012117.
A. Arifandi, “Identifikasi dan Prediksi Umur Serta Jenis Kelamin Berdasarkan Citra Wajah Menggunakan Algoritma Convolutional Neural Network (CNN),” J. Terap. Sains Teknol., vol. 4, no. 2, pp. 89–96, 2022, [Online]. Available: https://ejournal.unikama.ac.id/index.php/jtst/article/view/6985.
J. Qin, W. Pan, X. Xiang, Y. Tan, and G. Hou, “A biological image classification method based on improved CNN,” Ecol. Inform., vol. 58, no. January, p. 101093, 2020, doi: 10.1016/j.ecoinf.2020.101093.
C. Yu, R. Han, M. Song, C. Liu, and C. I. Chang, “A simplified 2D-3D CNN architecture for hyperspectral image classification based on spatial-spectral fusion,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 2485–2501, 2020, doi: 10.1109/JSTARS.2020.2983224.
N. K. Sai Yeshwanth Chaganti, Ipseeta Nanda, Koteswara Rao Pandi, Tavva GNRSN Prudhvith, “Image classification using SVM and CNN,” IEEE Xplore, pp. 1–10, 2018, [Online]. Available: https://rpubs.com/Sharon_1684/454441.
H. Gong et al., “Multiscale information fusion for hyperspectral image classification based on hybrid 2d‐3d cnn,” Remote Sens., vol. 13, no. 12, 2021, doi: 10.3390/rs13122268.
P. H. Saputro, D. P. Wijaya, M. G. Pradana, D. L. Tyas, and W. F. Zalmi, “Comparison ADAM-optimizer and SGDM for Classification Images of Rice Leaf Disease,” Proc. - 4th Int. Conf. Informatics, Multimedia, Cyber Inf. Syst. ICIMCIS 2022, pp. 348–353, 2022, doi: 10.1109/ICIMCIS56303.2022.10017644.
B. Rostami, D. M. Anisuzzaman, C. Wang, S. Gopalakrishnan, J. Niezgoda, and Z. Yu, “Multiclass wound image classification using an ensemble deep CNN-based classifier,” Comput. Biol. Med., vol. 134, no. May, p. 104536, 2021, doi: 10.1016/j.compbiomed.2021.104536.
U. Michelucci, Advanced Applied Deep Learning : Convolutional Neural Networks and Object Detection. Apress, 2018.
M. Elgendy, Deep Learning for Vision Systems. New York: Manning, 2020.
DOI: http://dx.doi.org/10.21927/ijubi.v6i2.3765
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Indonesian Journal of Business Intelligence (IJUBI)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
IJUBI by https://ejournal.almaata.ac.id/index.php/IJUBI is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats
Indonesian Journal of Business Intelligence (IJUBI)
Department of Information System
Alma Ata University
Email: ijubi@almaata.ac.id