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Recognition of Indonesian Traditional Cakes using The MobileNet Algorithm
Indonesia is a country with a variety of cultures, ranging from dance to cuisine and food variations. Cake is one of the unique variations of food include traditional cake. A variety of custom-made cakes will make the taste special, even though the name is the same. Traditional cakes are foods that are part of the ancestral culture that has been passed down from generation to generation explicitly in the region or Indonesian society. Machine learning methods are suitable for consistent and clear object recognition, this requires complex image pre-processing and feature extraction methods. The proposed model of our research is MobileNetv2 which was customized and then we did fine tuning then all of our training datasets do data-augmentation to create new datasets with various patterns so that the train dataset can be more numerous and avoid overfitting and the model can detect cake differences with an accuracy rate of 94% and loss 0.06
CNN, MobileNet Fine-Tuned, Traditional Cake.
- S. Wijaya, “Indonesian food culture mapping: A starter contribution to promote Indonesian culinary tourism,” J. Ethn. Foods, vol. 6, no. 1, pp. 1–10, 2019, doi: 10.1186/s42779-019-0009-3.
- D. J. Attokaren, I. G. Fernandes, A. Sriram, Y. V. S. Murthy, and S. G. Koolagudi, “Food classification from images using convolutional neural networks,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2017-Decem, pp. 2801–2806, 2017, doi: 10.1109/TENCON.2017.8228338.
- R. A. Rahmat and S. B. Kutty, “Malaysian food recognition using alexnet CNN and transfer learning,” ISCAIE 2021 - IEEE 11th Symp. Comput. Appl. Ind. Electron., pp. 59–64, 2021, doi: 10.1109/ISCAIE51753.2021.9431833.
- F. Aziz, D. Riana, J. Dwi Mulyanto, D. Nurrahman, and M. Tabrani, “Usability Evaluation of the Website Services Using the WEBUSE Method (A Case Study: covid19.go.id),” J. Phys. Conf. Ser., p. 12103, 2020, doi: 10.1088/1742-6596/1641/1/012103.
- K. Kiratiratanapruk, P. Temniranrat, A. Kitvimonrat, W. Sinthupinyo, and S. Patarapuwadol, “Using Deep Learning Techniques to Detect Rice Diseases from Images of Rice Fields,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12144 LNAI, pp. 225–237, Sep. 2020, doi: 10.1007/978-3-030- 55789-8_20.
- C. M. Annur, “Indonesia Peringkat ke-4 Negara Berpenduduk Terbanyak Dunia | Databoks,” katadata.co.id, 2020. https://databoks.katadata.co.id/datapublish/2020/12/15/indonesiaperingkat-ke-4-negara-berpenduduk-terbanyak-dunia (accessed Mar. 17, 2022).
- A. Wijaya, A. Rahmadi, E. Harmayani, and G. Djarkasi, The Uniqueness of ASEAN Food. GSS Djarkasi, 2021.
- T. Gressling, 84 Automated machine learning. 2020.
- S. S. Venkatesh, Nagaraju Y, Siddhanth U Hegde, “Fine-tuned MobileNet Classifier for Classification of Strawberry and Cherry Fruit Types,” 2021 Int. Conf. Comput. Commun. Informatics, vol. 1, no. 1, 2021.
- T. Karlita and I. Prasetyaningrum, “Indonesian Traditional Cake Classification Using Convolutional Neural Networks,” iCAST-SS 2021, vol. 647, pp. 924–929, 2022.
- Z. Abidin and R. Borman, “Classification of Indonesian Traditional Snacks Based on Image Using Convolutional Neural Network (CNN) Algorithm,” ieeexplore.ieee.org, vol. 1, no. 1, 2021, Accessed: Mar. 17, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9649707/.
- L. Quaranta, F. Calefato, and F. Lanubile, “KGTorrent: A dataset of python jupyter notebooks from kaggle,” Proc. - 2021 IEEE/ACM 18th Int. Conf. Min. Softw. Repos. MSR 2021, pp. 550–554, 2021, doi: 10.1109/MSR52588.2021.00072.
- M. Hussain, J. J. Bird, and D. R. Faria, “A study on CNN transfer learning for image classification,” Adv. Intell. Syst. Comput., vol. 840, pp. 191–202, 2019, doi: 10.1007/978-3-319-97982-3_16.
- ImageNet.org, “ILSVRC Competition,” ILSVRC, 2022. https://imagenet.org/challenges/LSVRC/ (accessed Mar. 21, 2022).
- P. G. J. and N. K. V., “Google Colaboratory : Tool for Deep Learning and Machine Learning Applications,” Indian J. Comput. Sci., vol. 6, no. 3–4, pp. 23–26, Aug. 2021, doi: 10.17010/IJCS/2021/V6/I3- 4/165408.
- Google Inc., “Overfit and Underfit at Deep Learning,” Google Colaboratory, 2018. https://colab.research.google.com/github/csahat/docs/blob/kerasoverfit-trans-id/site/id/tutorials/keras/overfit_and_underfit.ipynb (accessed Jan. 18, 2022).
- M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 4510– 4520, 2018, doi: 10.1109/CVPR.2018.00474.
- M. U. Hasan, “VGG16 - Convolutional Network for Classification and Detection,” neurohive.io, 2018. https://neurohive.io/en/popularnetworks/vgg16/ (accessed Jan. 14, 2022).
- P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Deep feature based rice leaf disease identification using support vector machine,” Comput. Electron. Agric., vol. 175, p. 105527, Aug. 2020, doi: 10.1016/J.COMPAG.2020.105527.
- C. Lee, H. J. Kim, and K. W. Oh, “Comparison of faster R-CNN models for object detection,” Int. Conf. Control. Autom. Syst., vol. 0, no. Iccas, pp. 107–110, 2016, doi: 10.1109/ICCAS.2016.7832305.
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