Implementasi Arsitektur EfficientNetV2 Untuk Klasifikasi Gambar Makanan Tradisional Indonesia
DOI:
https://doi.org/10.30865/mib.v7i2.5881Keywords:
Traditional Food, Indonesia, Deep Learning, Classification, EfficientNetV2Abstract
Indonesia has many variations of traditional food and interesting tourist destinations. The large number of tourist destinations make people like traveling and try to enjoy their traditional food. However, when trying traditional foods, especially foods that are new to them, they must be more careful, because the various ingredients contained in them have an impact on health. This research will try to make an application that can recognize Indonesian traditional food. The hope is that it can provide complete information, so that it can be used to develop calorie counter applications in the future. This study aims to design a system that can classify Indonesian traditional food images to help recognize food names with a certain level of accuracy using the EfficientNetV2 architecture. EfficientNetV2 is a new family of deep learning that excels in training as well as parameter efficiency. Deep Learning is a method often used to classify complex images. The EfficientNetV2 used in this study consists of four different architectures namely EfficientNetV2_S_21k, EfficientNetV2_M_21k, EfficientNetV2_L_21k, and EfficientNetV2_XL_21k. The dataset used comes from three types of data source categories, namely from Google Images, direct image capture using a Smartphone camera, and a combination of both. Each dataset category consists of 18 classes with a total of 1,800 images from Google Images, 1,800 images from Smartphone cameras, and 3,600 images from a combination of Google Images and Smartphone cameras. The dataset is taken from three categories to compare the level of accuracy and get the best accuracy value. The results of this study indicate that EfficientNetV2 can classify images of Indonesian traditional food with the highest test accuracy value of 99.4% from the EfficientNetV2-L(21k) model and the results obtained do not occur overfitting.References
D. A. Kurnia, A. Setiawan, D. R. Amalia, R. W. Arifin, and D. Setiyadi, “Image Processing Identification for Indonesian Cake Cuisine using CNN Classification Technique,†in Journal of Physics: Conference Series, IOP Publishing Ltd, Feb. 2021. doi: 10.1088/1742-6596/1783/1/012047.
C. Mahaputri, Y. Kristian, and E. Setyati, “Pengenalan Makanan Tradisional Indonesia Beserta Bahan-bahannya dengan Memanfaatkan DCNN Transfer Learning,†2022, doi: 10.52985/insyst.v4i2.252.
I. Pamelia, “Fast Food Consumption Behavior in Adolescent and ITS Impact for Health,†2018.
L. Pan, S. Pouyanfar, H. Chen, J. Qin, and S. C. Chen, “DeepFood: Automatic Multi-Class Classification of Food Ingredients Using Deep Learning,†in Proceedings - 2017 IEEE 3rd International Conference on Collaboration and Internet Computing, CIC 2017, Institute of Electrical and Electronics Engineers Inc., Dec. 2017, pp. 181–189. doi: 10.1109/CIC.2017.00033.
F. Akter, T. Khatun, and M. S. Uddin, “Recognition and Classification of Fast Food Images,†2018. [Online]. Available: https://www.researchgate.net/publication/350845216
W. Wang, Y. Xu, J. Shen, and S.-C. Zhu, “Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification,†2018.
B. Kolisnik, I. Hogan, and F. Zulkernine, “Condition-CNN: A Hierarchical Multilabel Fashion Image Classification Model,†Expert Syst Appl, vol. 182, Nov. 2021, doi: 10.1016/j.eswa.2021.115195.
M. Xin and Y. Wang, “Research on Image Classification Model based on Deep Convolution Neural Network,†EURASIP J Image Video Process, vol. 2019, no. 1, Dec. 2019, doi: 10.1186/s13640-019-0417-8.
J. R. Rajayogi, G. Manjunath, and G. Shobha, Indian Food Image Classification with Transfer Learning. 2019.
S. J. Park, A. Palvanov, C. H. Lee, N. Jeong, Y. I. Cho, and H. J. Lee, “The Development of Food Image Detection and Recognition Model of Korean Food for Mobile Dietary Management,†Nutr Res Pract, vol. 13, no. 6, pp. 521–528, Dec. 2019, doi: 10.4162/nrp.2019.13.6.521.
I. P. A. E. Udayana, M. Sudarma, and P. G. S. C. Nugraha, “Implementation of Convolutional Neural Networks to Recognize Images of Common Indonesian Food,†in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, May 2020. doi: 10.1088/1757-899X/846/1/012023.
A. Ramdani, A. Virgono, and C. Setianingsih, Food Detection with Image Processing using Convolutional Neural Network (CNN) Method. 2020.
G. Thiodorus, A. Prasetia, L. A. Ardhani, and N. Yudistira, “Klasifikasi Citra Makanan/Non Makanan menggunakan Metode Transfer Learning dengan model Residual Network,†Teknologi, vol. 11, no. 2, pp. 74–83, Jul. 2021, doi: 10.26594/teknologi.v11i2.2402.
A. Rohim, Y. A. Sari, and Tibyani, “Convolution Neural Network (CNN) untuk Pengklasifikasian Citra Makanan Tradisional,†2019. [Online]. Available: http://j-ptiik.ub.ac.id
M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,†May 2019, [Online]. Available: http://arxiv.org/abs/1905.11946
M. Tan and Q. V. Le, “EfficientNetV2: Smaller Models and Faster Training,†Apr. 2021, [Online]. Available: http://arxiv.org/abs/2104.00298
R. Karthik, T. S. Vaichole, S. K. Kulkarni, O. Yadav, and F. Khan, “Eff2Net: An Efficient Channel Attention-based Convolutional Neural Network for Skin Disease Classification,†Biomed Signal Process Control, vol. 73, Mar. 2022, doi: 10.1016/j.bspc.2021.103406.
S. Fan, R. Xu, and Z. Yan, “A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer,†Sep. 2021, doi: 10.1109/CISP-BMEI53629.2021.9624252.
Y. Ye, H. Zhou, H. Yu, H. Hu, G. Zhang, J. Hu, and T. He, “An Improved EfficientNetV2 Model Based on Visual Attention Mechanism: Application to Identification of Cassava Disease,†Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/1569911.
C. K. Sunil, C. D. Jaidhar, and N. Patil, “Cardamom Plant Disease Detection Approach Using EfficientNetV2,†IEEE Access, vol. 10, pp. 789–804, 2022, doi: 10.1109/ACCESS.2021.3138920.
J. Brownlee, “Deep Learning for Computer Vision Image Classification, Object Detection and Face Recognition in Python,†2019.
T. Beysolow II, Applied Natural Language Processing with Python. Apress, 2018. doi: 10.1007/978-1-4842-3733-5.
L. Ying, Z. Nan, W. Ping, C. Kiang, L. Pang, Z. Chang, C. Lu, L. Jun, and L. Nam, “Adaptive Weights Learning in CNN Feature Fusion for Crime Scene Investigation Image Classification,†Conn Sci, vol. 33, no. 3, pp. 719–734, 2021, doi: 10.1080/09540091.2021.1875987.
D. Padilla, A. Yumang, A. L. Diaz, and G. Inlong, Differentiating Atopic Dermatitis and Psoriasis Chronic Plaque using Convolutional Neural Network MobilNet Architecture. 2019.
M. D. Darojat, Y. A. Sari, and R. C. Wihandika, “Convolutional Neural Network untuk Klasifikasi Citra Makanan Khas Indonesia,†2021. [Online]. Available: http://j-ptiik.ub.ac.id
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