Eksperimen Layer Pooling menggunakan Standar Deviasi untuk Klasifikasi Dataset Citra Wajah dengan Metode CNN
- Yovi Pratama Universitas Dinamika Bangsa, Jambi, Indonesia
-
Errissya Rasywir
*
Universitas Dinamika Bangsa, Jambi,
Indonesia
- Fachruddin Fachruddin Universitas Dinamika Bangsa, Jambi, Indonesia
- Desi Kisbianty Universitas Dinamika Bangsa, Jambi, Indonesia
- Beni Irawan Universitas Dinamika Bangsa, Jambi, Indonesia (*) Corresponding Author
Abstract
Deep Learning, especially the Convolutional Neural Network (CNN) has proven to be reliable in processing data from various programming language platforms by utilizing deep learning. In this study, we modified it by calculating the statistical variance. The modifications made are replacing calculations on the Pooling Layer which generally use two formulas, namely max pooling and average pooling. We use the standard deviation to change the reduced image intensity value. With the research experiments built, it is expected to be able to perform facial recognition as an indicator for testing modifications. The Layer Pooling experiment uses the Standard Deviation for Classifying Face Image Datasets with the CNN Method, including the type of dataset used is the Aberdeen dataset https://pics.stir.ac.uk/2D_face_sets.htm. From the results of the experiments conducted, it was found that the highest value was using the Elu activation function and the Adagrad optimizer worth 77.844% for max pooling and 79.844% for pooling with a standard deviation. The Cellu activation function and the RMSprop optimizer are 77.986% for max pooling and 75.986% for pooling with a standard deviation. The highest score with the Softplus activation function and the Sgd optimizer is 77.844% for max pooling usage and 76.344% for pooling with standard deviation. The Tanh activation function and the Adadelta optimizer are 87.844% for max pooling and 85.844% for pooling with a standard deviation. The Elu activation function and the Adam optimizer are 87.853% for the use of max pooling and 85.285% for pooling with a standard deviation. By using the Elu activation function and the Adamax optimizer, the value is 87.842% for max pooling and 86.242% for pooling with a standard deviation. The highest score is using the Elu activation function and the Nadam optimizer with a value of 87.845% for max pooling usage and 86.345% for using standard deviation calculations as pixel pooling. From all experiments it was stated that the use of pooling with the highest value technique or max pooling still gave a better value than using the standard deviation calculation with the best tuning results using the Elu activation function and Adam's Optimiser, which was 87.853%.
Downloads
References
Fachruddin, Saparudin, E. Rasywir, and Y. Pratama, “Network and layer experiment using convolutional neural network for content based image retrieval work,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 1, pp. 118–128, 2022, doi: 10.12928/TELKOMNIKA.v20i1.19759.
Y. Pratama and E. Rasywir, “Eksperimen Penerapan Sistem Traffic Counting dengan Algoritma YOLO (You Only Look Once) V.4.,” J. Media Inform. Budidarma, vol. 5, no. 4, p. 1438, 2021, doi: 10.30865/mib.v5i4.3309.
Z. Wu et al., “MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection,” Phys. Med. Biol., vol. 65, no. 23, p. 235053, Dec. 2020, doi: 10.1088/1361-6560/aba87c.
T. M. Tamtelahitu, “Komparasi Algoritma Clustering dengan Dataset Penyebaran Covid-19 di Indonesia Periode Maret-Mei 2020,” J. Teknol. Technoscientia, vol. 13, no. 1, pp. 27–34, 2020.
Fachruddin, Y. Pratama, E. Rasywir, D. Kisbianty, Hendrawan, and M. R. Borroek, “Real Time Detection on Face Side Image with Ear Biometric Imaging Using Integral Image and Haar-Like Feature,” in Proceedings of 2018 International Conference on Electrical Engineering and Computer Science, ICECOS 2018, 2019, pp. 165–170, doi: 10.1109/ICECOS.2018.8605218.
G. Aprilian Anarki, K. Auliasari, and M. Orisa, “PENERAPAN METODE HAAR CASCADE PADA APLIKASI DETEKSI MASKER,” JATI (Jurnal Mhs. Tek. Inform., vol. 5, no. 1, pp. 179–186, 2021, doi: 10.36040/jati.v5i1.3214.
S. Kornblith, J. Shlens, and Q. V. Le, “Do better imagenet models transfer better?,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2019-June, pp. 2656–2666, 2019, doi: 10.1109/CVPR.2019.00277.
M. Ju, H. Luo, Z. Wang, B. Hui, and Z. Chang, “The application of improved YOLO V3 in multi-scale target detection,” Appl. Sci., vol. 9, no. 18, 2019, doi: 10.3390/app9183775.
H. Bunyamin, Heriyanto, S. Novianti, and L. Sulistiani, “Topic clustering and classification on final project reports: A comparison of traditional and modern approaches,” IAENG Int. J. Comput. Sci., vol. 46, no. 3, pp. 1–6, 2019.
G. Liu, J. C. Nouaze, P. L. T. Mbouembe, and J. H. Kim, “YOLO-tomato: A robust algorithm for tomato detection based on YOLOv3,” Sensors (Switzerland), vol. 20, no. 7, pp. 1–20, 2020, doi: 10.3390/s20072145.
S. K. Yoki Donzia and H. K. Kim, “Implementation of recurrent neural network with sequence to sequence model to translate language based on tensorflow,” Lect. Notes Eng. Comput. Sci., vol. 2237, pp. 375–379, 2018.
P. Wanda and H. J. Jie, “DeepSentiment: Finding malicious sentiment in online social network based on dynamic deep learning,” IAENG Int. J. Comput. Sci., vol. 46, no. 4, pp. 1–12, 2019.
Z. Wu et al., “MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection,” Phys. Med. Biol., vol. 65, no. 23, p. 235053, 2020, doi: 10.1088/1361-6560/aba87c.
K. Celikmih, O. Inan, and H. Uguz, “Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method,” Sci. Program., vol. 2020, pp. 1–10, 2020, doi: 10.1155/2020/8616039.
A. P. Rodrigues et al., “Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/5211949.
X. Li, L. Bing, W. Lam, and B. Shi, “Transformation Networks for Target-Oriented Sentiment Classification,” arxiv, 2018.
D. Zhao, Y. Liu, H. Yin, and Z. Wang, “A novel multi-scale CNNs for false positive reduction in pulmonary nodule detection,” Expert Syst. Appl., vol. 207, p. 117652, 2022, doi: https://doi.org/10.1016/j.eswa.2022.117652.
D. Z. Abidin, S. Nurmaini, Erwin, E. Rasywir, and Y. Pratama, “Indoor Positioning System in Learning Approach Experiments,” J. Electr. Comput. Eng., vol. 2021, 2021, doi: 10.1155/2021/6592562.
S. Nurmaini, A. Darmawahyuni, A. N. S. Mukti, M. N. Rachmatullah, F. Firdaus, and B. Tutuko, “Deep learning-based stacked denoising and autoencoder for ECG heartbeat classification,” Electron., vol. 9, no. 1, 2020, doi: 10.3390/electronics9010135.
A. B. Adege, H. P. Lin, G. B. Tarekegn, Y. Y. Munaye, and L. Yen, “An indoor and outdoor positioning using a hybrid of support vector machine and deep neural network algorithms,” J. Sensors, vol. 2018, 2018, doi: 10.1155/2018/1253752.
Y. Wang, J. Gao, Z. Li, and L. Zhao, “Robust and accurate Wi-Fi fingerprint location recognition method based on deep neural network,” Appl. Sci., vol. 10, no. 1, 2020, doi: 10.3390/app10010321.
J. Yu, B. Yang, J. Wang, J. K. Leader, D. O. Wilson, and J. Pu, “2D CNN versus 3D CNN for false-positive reduction in lung cancer screening,” J. Med. Imaging, vol. 7, no. 5, p. 51202, 2020, doi: 10.1117/1.JMI.7.5.051202.
F. Ullah, M. R. Naeem, L. Mostarda, and S. A. Shah, “Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model,” Int. J. Mach. Learn. Cybern., 2021, doi: 10.1007/s13042-020-01246-9.
Y. Wang et al., “EV-gait: Event-based robust gait recognition using dynamic vision sensors,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2019-June, pp. 6351–6360, 2019, doi: 10.1109/CVPR.2019.00652.
D. Spatharakis et al., “A scalable Edge Computing architecture enabling smart offloading for Location Based Services,” Pervasive Mob. Comput., vol. 67, p. 101217, 2020, doi: 10.1016/j.pmcj.2020.101217.
Hendry and R. C. Chen, “Using deep learning to predict user rating on imbalance classification data,” IAENG Int. J. Comput. Sci., vol. 46, no. 1, 2019.
Q. Zheng, X. Tian, M. Yang, and H. Wang, “Differential learning: A powerful tool for interactive content-based image retrieval,” Eng. Lett., vol. 27, no. 1, pp. 202–215, 2019.
N. Akhtar and U. Ragavendran, “Interpretation of intelligence in CNN-pooling processes: a methodological survey,” Neural Comput. Appl., vol. 32, no. 3, pp. 879–898, 2020, doi: 10.1007/s00521-019-04296-5.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Eksperimen Layer Pooling menggunakan Standar Deviasi untuk Klasifikasi Dataset Citra Wajah dengan Metode CNN
Pages: 200−210
Copyright (c) 2023 Yovi Pratama, Errissya Rasywir, Fachruddin, Desi Kisbianty, Beni Irawan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
