Performance Analysis of Convolutional Neural Network in Pempek Food Image Classification with MobileNetV2 and GoogLeNet Architecture
DOI:
https://doi.org/10.51519/journalisi.v7i1.1026Keywords:
Food classification, Deep Learning, CNN, MobileNetV2, GoogLeNet, pempekAbstract
This research develops a pempek food image classification system using two Deep Learning architectures, namely MobileNetV2 and GoogLeNet. The dataset consists of five types of pempek with a total of 446 images, which are divided for training (70%), validation (15%), and testing (15%). The model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that GoogLeNet achieved a validation accuracy of 96.21%, higher than MobileNetV2 which was only 70.58%. GoogLeNet is also more stable in convergence and more accurate in recognizing different types of pempek. This research shows that GoogLeNet is more optimal for pempek classification. In the future, this research can be extended by adding more datasets, exploring more sophisticated models, and developing mobile or web-based applications.
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A. Nasir, D. Dasir, dan S. Patimah, "Nilai sensoris aroma dan rasa pempek dari jenis olahan daging ikan patin (Pangasius pangasius) dan perbandingan tepung tapioka," Edible: J. Penelit. Ilmu-Ilmu Teknol. Pangan, vol. 8, no. 1, Art. no. 1, Jun. 2021, doi: 10.32502/JEDB.V8i1.3442.
S. Patel dan S. S., "Performance evaluation of feature extraction algorithms for vehicle shape classification," U.Porto J. Eng., vol. 8, no. 6, Art. no. 6, Nov. 2022, doi: 10.24840/2183-6493_008.006_0005.
H. Kagaya, K. Aizawa, dan M. Ogawa, "Food detection and recognition using convolutional neural network," Proc. 22nd ACM Int. Conf. Multimedia (MM '14), New York, NY, USA: Assoc. Comput. Mach., Nov. 2014, pp. 1085–1088, doi: 10.1145/2647868.2654970.
K.-S. Lee, "Multispectral food classification and caloric estimation using convolutional neural networks," Foods, vol. 12, no. 17, Art. no. 17, Jan. 2023, doi: 10.3390/Foods12173212.
N. Begum dan M. K. Hazarika, "Deep learning-based image processing solutions in food engineering: A review," Agric. Res., Sep. 2021, doi: 10.18805/Ag.R-2182.
N. J. Ezeora, O. N. Emeka, E. V. Ebere, dan N. J. Ngene, "Real-time food recognition and documentation Android system for the learning of Nigerian foods using deep learning method," Int. J. Adv. Res. Comput. Commun. Eng., vol. 11, no. 12, Nov. 2022, doi: 10.17148/IJARCCE.2022.111201.
A. Singla, L. Yuan, dan T. Ebrahimi, "Food/non-food image classification and food categorization using pre-trained GoogLeNet model," Proc. 2nd Int. Workshop Multimedia Assist. Dietary Manag., Amsterdam, Netherlands: ACM, Oct. 2016, pp. 3–11, doi: 10.1145/2986035.2986039.
P. R. Togatorop, Y. Pratama, A. M. Sianturi, M. S. Pasaribu, dan P. S. Sinaga, "Image preprocessing and hyperparameter optimization on pretrained model MobileNetV2 in white blood cell image classification," IAES Int. J. Artif. Intell. (IJ-AI), vol. 12, no. 3, Art. no. 3, Sep. 2023, doi: 10.11591/IJAI.V12.I3.PP1210-1223.
T. Xue, H. Wang, dan D. Wu, "MobileNetV2 combined with fast spectral kurtosis analysis for bearing fault diagnosis," Electronics, vol. 11, no. 19, p. 3176, Oct. 2022, doi: 10.3390/Electronics11193176.
C. Szegedy et al., "Going deeper with convolutions," arXiv, Sep. 17, 2014, arXiv:1409.4842, doi: 10.48550/arXiv.1409.4842.
E. T. Yasin, R. Kursun, dan M. Koklu, "Deep learning-based classification of black gram plant leaf diseases: A comparative study," Proc. Int. Conf. Adv. Technol., vol. 11, pp. 1–8, Sep. 2023, doi: 10.58190/ICAT.2023.9.
R. Rawat, J. K. Patel, dan M. T. Manry, "Minimizing validation error with respect to network size and number of training epochs," Proc. 2013 Int. Joint Conf. Neural Netw. (IJCNN), Dallas, TX, USA: IEEE, Aug. 2013, pp. 1–7, doi: 10.1109/IJCNN.2013.6706919.
D. P. Kingma dan J. Ba, "Adam: A method for stochastic optimization," arXiv, Jan. 29, 2017, arXiv:1412.6980, doi: 10.48550/arXiv.1412.6980.
H. M dan S. M. N, "A review on evaluation metrics for data classification evaluations," Int. J. Data Knowl. Process. (IJDKP), vol. 5, no. 2, pp. 01–11, Mar. 2015, doi: 10.5121/IJDKP.2015.5201.
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