Padang Cuisine Classification Using Resnet Features And Transfer Learning
Keywords:
Padang Cuisine Classification, Transfer Learning, ResNet, Support Vector Machine, Multi-Layer PerceptronAbstract
Padang cuisine is a renowned Indonesian culinary heritage with diverse dishes that are often challenging to identify visually due to their similarities and variations. This study proposes a digital image-based classification system to accurately recognize five types of Padang dishes: Rendang, Dendeng Batokok, Ayam Pop, Gulai Tambusu, and Gulai Tunjang. Feature extraction was performed using a pre-trained ResNet model to leverage its deep residual architecture, which effectively captures rich visual information. The extracted features were then used to train and compare two classifiers: Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, and Multi-Layer Perceptron (MLP) with three hidden layers and ReLU activation optimized by Adam. The dataset comprised 548 images collected from Kaggle, evenly distributed across the five dish categories. Evaluation metrics included accuracy, precision, and recall. Experimental results show that SVM achieved the highest performance with an accuracy of 87.9%, outperforming MLP, which obtained 87.3%. The findings suggest that SVM with RBF kernel is more suitable for classifying Padang dishes on a limited dataset, while MLP holds potential with further optimization and larger datasets. This research contributes to the advancement of automated culinary recognition systems, supporting the preservation and promotion of Indonesian culinary heritage through artificial intelligence.
Keywords : Padang Cuisine Classification, Transfer Learning, ResNet, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP)
References
Abdel-aziem, A. H. A., & Soliman, T. H. M. (2023). A Multi-Layer Perceptron (MLP) Neural Networks for Stellar Classification: A Review of Methods and Results. International Journal of Advances in Applied Computational Intelligence, 3(2), 29–37. https://doi.org/10.54216/IJAACI.030203
Afrinanto, F. F. (2022). Padang Cuisine (Indonesian Food Image Dataset). https://www.kaggle.com/datasets/faldoae/padangfood
Al Bataineh, A., Kaur, D., & Jalali, S. M. J. (2022). Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing. IEEE Access, 10, 36963–36977. https://doi.org/10.1109/ACCESS.2022.3164669
Albert, P., Ortego, D., Arazo, E., O’Connor, N., & McGuinness, K. (2021). ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning. Proceedings of the International Joint Conference on Neural Networks, 2021-July. https://doi.org/10.1109/IJCNN52387.2021.9533616
Andrianto, F., Fadlil, A., & Riadi, I. (2024). Linear Kernel Optimization of Support Vector Machine Algorithm on Online Marketplace Sentiment Analysis. Komputasi: Jurnal Ilmiah Ilmu Komputer Dan Matematika, 21(1), 68–82. https://doi.org/10.33751/komputasi.v21i1.9266
Bai, Y. (2022). RELU-Function and Derived Function Review. SHS Web of Conferences, 144, 02006. https://doi.org/10.1051/shsconf/202214402006
Duta, I. C., Liu, L., Zhu, F., & Shao, L. (2020). Improved residual networks for image and video recognition. Proceedings - International Conference on Pattern Recognition, 9415–9422. https://doi.org/10.1109/ICPR48806.2021.9412193
Faridah, A., Utami, R. G., Parmanoan, D., Rahmatunisa, R., Adrian, A., Rosel, R. F., & Huda, N. (2024). The Global Research Landscape of Rendang: A Bibliometric Insight into Culinary Heritage and Innovation. Pharmacognosy Journal , 16(6), 1320–1330. https://doi.org/10.5530/pj.2024.16.213
Mehta, A., Sengupta, P., Garg, D., Singh, H., & Diamand, Y. S. (2023). Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans. http://arxiv.org/abs/2307.07863
Nhaila, H., Elmaizi, A., Sarhrouni, E., & Hammouch, A. (2019). Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information. Procedia Computer Science, 148, 97–106. https://doi.org/10.1016/j.procs.2019.01.013
Pardede, D., Ichsan, A., & Riyadi, S. (2024). Enhancing Multi-Layer Perceptron Performance with K-Means Clustering. Journal of Computer Networks, Architecture and High Performance Computing, 6(1), 461–466. https://doi.org/10.47709/cnahpc.v6i1.3600
Pardede, D., Wanayumini, & Rosnelly, R. (2023). A Combination Of Support Vector Machine And Inception-V3 In Face-Based Gender Classification. International Conference on Information Science and Technology Innovation (ICoSTEC), 2(1), 34–39. https://doi.org/10.35842/icostec.v2i1.30
Prajena, G., Harefa, J., Alexander, Josephus, B. O., & Nawir, A. H. (2022). Indonesian Traditional Food Image Recognition using Convolutional Neural Network. 2022 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 142–147. https://doi.org/10.1109/ICIMCIS56303.2022.10017684
Pugra, I. W., Sutiarso, M. A., & Joaquin, C. J. (2025). The cultural significance of traditional foods in shaping Indonesian social identity: Challenges and preservation strategies. Journal of Language, Literature, Social and Cultural Studies, 3(1), 21–31. https://doi.org/10.58881/jllscs.v3i1.318
Rangkuti, A. H., Kerta, J. M., Mogot, R. Y., & Athala, V. H. (2024). Identification of Indonesian Traditional Foods Using Machine Learning and Supported by Segmentation Methods. International Journal on Informatics Visualization, 8(4), 2324–2335. https://doi.org/10.62527/joiv.8.4.2545
Sekar Sari, M., Widyatmaja, I. G. N., & Yanthy, I. P. S. (2023). Culinary Heritage as Tourist Attraction in Kota Tua, Padang-West Sumatera. Asian Journal of Social and Humanities, 2(2), 1687–1701. https://doi.org/10.59888/ajosh.v2i2.158
Shafiq, M., & Gu, Z. (2022). Deep Residual Learning for Image Recognition: A Survey. Applied Sciences (Switzerland), 12(18), 1–43. https://doi.org/10.3390/app12188972
Sharma, A., Rani, S., Bashir, A. K., Krichen, M., & Alshammari, A. (2023). A Low-Rank Learning-Based Multi-Label Security Solution for Industry 5.0 Consumers Using Machine Learning Classifiers. IEEE Transactions on Consumer Electronics, 69(4), 833–841. https://doi.org/10.1109/TCE.2023.3282964
Wulandari, A. (2024). Comparing CNN Architecture for Indonesian Speciality Cuisine Classification. Engineering, MAthematics and Computer Science Journal (EMACS), 6(1), 55–60. https://doi.org/10.21512/emacsjournal.v6i1.11076
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