Klasifikasi Citra Ikan Menggunakan Algoritma Convolutional Neural Network dengan Arsitektur VGG-16
Keywords:Image Classification of Fish; Convolutional Neural Networks; VGG-16 Architecture; Classification Accuracy; Image Analysis
The development of technology in the field of image processing has inspired this research which aims to overcome challenges in classifying fish images using a Convolutional Neural Network (CNN) based approach. In this research, we utilize the VGG-16 architecture, a CNN model that has been proven capable of retrieving important features from images with significant depth. The dataset consists of 1088 fish images divided into four classes: Bangus, Glass Perchlet, Gold Fish, and Gourami. The initial process involves feature extraction via image embedding using the VGG-16 architecture. Next, a classification model is built using the Orange Data Mining tool. The experimental results show that this approach is able to provide good classification performance with significant accuracy in recognizing different fish species. The use of VGG-16 enables powerful and complex feature extraction, and experimental results show that this approach achieves a training accuracy of 96.2%. Furthermore, when the classification process uses data testing, this method produces an accuracy of 99.5%. This finding shows the great potential of the Convolutional Neural Network in overcoming the challenge of classifying fish images with very satisfactory results, which can be applied in various fields including marine science and remote sensing.
X. Feng, Y. Jiang, X. Yang, M. Du, and X. Li, “Computer vision algorithms and hardware implementations: A survey,” Integration, vol. 69. 2019. doi: 10.1016/j.vlsi.2019.07.005.
M. H. Guo et al., “Attention mechanisms in computer vision: A survey,” Computational Visual Media, vol. 8, no. 3. 2022. doi: 10.1007/s41095-022-0271-y.
M. Abdullah-Al-Noman, A. N. Eva, T. B. Yeahyea, and R. Khan, “Computer Vision-based Robotic Arm for Object Color, Shape, and Size Detection,” Journal of Robotics and Control (JRC), vol. 3, no. 2, 2022, doi: 10.18196/jrc.v3i2.13906.
A. Sophokleous, P. Christodoulou, L. Doitsidis, and S. A. Chatzichristofis, “Computer vision meets educational robotics,” Electronics (Switzerland), vol. 10, no. 6. 2021. doi: 10.3390/electronics10060730.
I. Muslem, “Prototype Kunci RFID (Radio Frequency Identification) dalam Meningkatkan Keamanan Kendaraan Bermotor,” JURNAL TIKA, vol. 5, no. 3, 2021, doi: 10.51179/tika.v5i3.104.
L. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, “Review of image classification algorithms based on convolutional neural networks,” Remote Sensing, vol. 13, no. 22. 2021. doi: 10.3390/rs13224712.
E. Riana, “Penerapan Sensor Ultrasonic SRF05 Berbasis Mikrocontroller ATMega 8535 Untuk Sistem Pengereman Otomatis,” Journal of Information System Research (JOSH), vol. 2, no. 4, 2021, doi: 10.47065/josh.v2i4.761.
B. P. Kumar, “Minimum Distance Warning and Braking System for Vehicles,” Int J Res Appl Sci Eng Technol, vol. 9, no. VI, 2021, doi: 10.22214/ijraset.2021.35506.
P. P. Freitas et al., “Giant magnetoresistive sensors for rotational speed control,” J Appl Phys, vol. 85, no. 8 II B, 1999, doi: 10.1063/1.369975.
S. J. Anand, A. M. Kumar, Mahendran, K. Pugalarasu, and K. Surya, “Drowsy and Drunken Drive Control, Automatic Accident Detection and Rescue System Using Arduino,” International Journal of Scientific Development and Research, vol. 5, no. 12, 2020.
A. Rahman, A. K. M. Mohiuddin, and A. Sakif, “Development of electro-hydro automatic parking braking system for automotive system,” International Journal of Recent Technology and Engineering, vol. 7, no. 6, 2019.
A. V. Postolit, “Prospects for the Use of Artificial Intelligence and Computer Vision in Transport Systems and Connected Cars,” World of Transport and Transportation, vol. 19, no. 1, 2021, doi: 10.30932/1992-3252-2021-19-1-74-90.
C. McCarroll and F. Cugurullo, “No city on the horizon: Autonomous cars, artificial intelligence, and the absence of urbanism,” Frontiers in Sustainable Cities, vol. 4, 2022, doi: 10.3389/frsc.2022.937933.
B. Padmaja, C. V. K. N. S. N. Moorthy, N. Venkateswarulu, and M. M. Bala, “Exploration of issues, challenges and latest developments in autonomous cars,” J Big Data, vol. 10, no. 1, 2023, doi: 10.1186/s40537-023-00701-y.
R. Hussain and S. Zeadally, “Autonomous Cars: Research Results, Issues, and Future Challenges,” IEEE Communications Surveys and Tutorials, vol. 21, no. 2. 2019. doi: 10.1109/COMST.2018.2869360.
P. P. Em, J. Hossen, I. Fitrian, and E. K. Wong, “Vision-based lane departure warning framework,” Heliyon, vol. 5, no. 8, 2019, doi: 10.1016/j.heliyon.2019.e02169.
W. Chen, W. Wang, K. Wang, Z. Li, H. Li, and S. Liu, “Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review,” Journal of Traffic and Transportation Engineering (English Edition), vol. 7, no. 6. 2020. doi: 10.1016/j.jtte.2020.10.002.
J. M. Clanton, D. M. Bevly, and A. S. Hodel, “A low-cost solution for an integrated multisensor lane departure warning system,” IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 1, 2009, doi: 10.1109/TITS.2008.2011690.
J. Wu, H. Xu, and J. Zhao, “Automatic Lane Identification Using the Roadside LiDAR Sensors,” IEEE Intelligent Transportation Systems Magazine, vol. 12, no. 1, 2020, doi: 10.1109/MITS.2018.2876559.
K. Simonyan and A. Zisserman, “VGG-16,” arXiv preprint, 2014.
D. Theckedath and R. R. Sedamkar, “Detecting Affect States Using VGG16, ResNet50 and SE-ResNet50 Networks,” SN Comput Sci, vol. 1, no. 2, 2020, doi: 10.1007/s42979-020-0114-9.
L. Mohammadpour, T. C. Ling, C. S. Liew, and A. Aryanfar, “A Survey of CNN-Based Network Intrusion Detection,” Applied Sciences (Switzerland), vol. 12, no. 16. 2022. doi: 10.3390/app12168162.
N. Gozzi et al., “Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs,” Diagnostics, vol. 12, no. 9, 2022, doi: 10.3390/diagnostics12092084.
M. Jiang, P. Wu, and F. Li, “Detecting dark spot eggs based on CNN GoogLeNet model,” Wireless Networks, 2021, doi: 10.1007/s11276-021-02673-4.
S. H. Noh, “Performance comparison of CNN models using gradient flow analysis,” Informatics, vol. 8, no. 3, 2021, doi: 10.3390/informatics8030053.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015. doi: 10.1109/CVPR.2015.7298965.
T. C. Lu, “CNN Convolutional layer optimisation based on quantum evolutionary algorithm,” Conn Sci, vol. 33, no. 3, 2021, doi: 10.1080/09540091.2020.1841111.
W. Yuan, B. Dong, S. Wang, M. Unoki, and W. Wang, “Evolving multi-resolution pooling cnn for monaural singing voice separation,” IEEE/ACM Trans Audio Speech Lang Process, vol. 29, 2021, doi: 10.1109/TASLP.2021.3051331.
D. Boob, S. S. Dey, and G. Lan, “Complexity of training ReLU neural network,” Discrete Optimization, vol. 44, 2022, doi: 10.1016/j.disopt.2020.100620.
Q. Gong, W. Kang, and F. Fahroo, “Approximation of compositional functions with ReLU neural networks,” Syst Control Lett, vol. 175, 2023, doi: 10.1016/j.sysconle.2023.105508.
R. Elshamy, O. Abu-Elnasr, M. Elhoseny, and S. Elmougy, “Improving the efficiency of RMSProp optimizer by utilizing Nestrove in deep learning,” Sci Rep, vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-35663-x.
D. Xu, S. Zhang, H. Zhang, and D. P. Mandic, “Convergence of the RMSProp deep learning method with penalty for nonconvex optimization,” Neural Networks, vol. 139, 2021, doi: 10.1016/j.neunet.2021.02.011.
V. V. Ramalingam and R. Ragavendran, “Prediction of liver disease using artificial neural network with adam optimizer,” Journal of Critical Reviews, vol. 7, no. 17, 2020, doi: 10.31838/jcr.07.17.164.
Z. Zhang, “Improved Adam Optimizer for Deep Neural Networks,” in 2018 IEEE/ACM 26th International Symposium on Quality of Service, IWQoS 2018, 2019. doi: 10.1109/IWQoS.2018.8624183.
J. H. Friedman, “Stochastic gradient boosting,” Comput Stat Data Anal, vol. 38, no. 4, 2002, doi: 10.1016/S0167-9473(01)00065-2.
S. Klein, J. P. W. Pluim, M. Staring, and M. A. Viergever, “Adaptive stochastic gradient descent optimisation for image registration,” Int J Comput Vis, vol. 81, no. 3, 2009, doi: 10.1007/s11263-008-0168-y.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasifikasi Citra Ikan Menggunakan Algoritma Convolutional Neural Network dengan Arsitektur VGG-16
Copyright (c) 2023 Imam Muslem R, Teuku Muhammad Johan, Luthfi
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).