Klasifikasi Jajanan Tradisional Indonesia berbasis Deep Learning dan Metode Transfer Learning

RAIHAN FATURRAHMAN, YULI SUN HARIYANI, SUGONDO HADIYOSO

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ABSTRAK

Makanan jajanan tradisional Indonesia telah menjadi warisan budaya yang berharga dan penting. Namun di tengah kemajuan zaman, sebagian masyarakat menganggapnya ketinggalan dan beralih ke makanan modern. Sebagai bagian dari upaya untuk melestarikan dan membantu masyarakat terutama kaum muda untuk mengenali ragam jajanan tradisional Indonesia, maka penelitian ini bertujuan untuk mengklasifikasi jenis jajanan tradisional Indonesia secara otomatis berdasarkan citra dengan menggunakan arsitektur deep learning. Dalam penelitian ini, dilakukan penggunaan metode transfer learning untuk melatih ulang basenetwork, sehingga mampu mengenali citra jajanan tradisional Indonesia. Di antara tiga base network yang dilatih dan diuji, disimpulkan bahwa dengan menggunakan base-network mobilenetV2 menghasilkan akurasi uji sebesar 98%, tertinggi dibandingkan dengan menggunakan ResNet50 dan VGG16 yang menghasilkan akurasi uji 97.33% dan 93.33%.

Kata kunci: jajanan tradisional indonesia, klasifikasi, deep learning, transfer learning

 

ABSTRACT

Traditional Indonesian snacks have become valuable and important cultural heritage. However, amidst the progress of time, some people consider them outdated and switch to modern foods. As part of an effort to preserve and help the community, especially the younger generation, to recognize various traditional Indonesian snacks, this research aims to automatically classify types of traditional Indonesian snacks based on images using deep learning architecture. In this study, transfer learning method was employed to retrain the base-network, enabling it to recognize images of traditional Indonesian snacks. Among the three base networks trained and tested, it was concluded that using the MobileNetV2 base-network resulted in a test accuracy of 98%, the highest compared to using ResNet50 and VGG16, which achieved test accuracies of 97.33% and 93.33% respectively.

Keywords: Indoensian traditional snack, classification, deep learning, transfer learning


Kata Kunci


jajanan tradisional indonesia; klasifikasi; deep learning; transfer learning

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Referensi


Fahira, P. K., Rahmadhani, Z. P., Mursanto, P., Wibisono, A., & Wisesa, H. A. (2020). Classical Machine Learning Classification for Javanese Traditional Food Image. ICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences, (pp.1–5). https://doi.org/10.1109/ICICoS51170.2020.9299039

Febriani, F. D., Sari, Y. A., & Wihandika, R. C. (2019). Klasifikasi Citra Kue Tradisional Indonesia Berdasarkan Ekstraksi Fitur Warna RGB Color Moment Menggunakan KNearest Neighbor. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(10), 10199–10206.

Hariman, A. A., Mulyana, D. I., & Yel, M. B. (2023). Klasifikasi Jajanan Tradisional Jawa Tengah dengan Metode Transfer Learning dan MobileNetV2. Jurnal Informasi Interaktif, 8(1), 15–23.

Hariyani, Y. S., Eom, H., & Park, C. (2020). DA-Capnet: Dual Attention Deep Learning Based on U-Net for Nailfold Capillary Segmentation. IEEE Access, 8, 10543–10553. https://doi.org/10.1109/ACCESS.2020.2965651

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 2261–2269). https://doi.org/10.1109/CVPR.2017.243

Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 1–12. https://doi.org/10.1016/j.jafr.2020.100033

Kaur, G., Sinha, R., Tiwari, P. K., Yadav, S. K., Pandey, P., Raj, R., Vashisth, A., & Rakhra, M. (2022). Face mask recognition system using CNN model. Neuroscience Informatics, 2(3), 100035. https://doi.org/10.1016/j.neuri.2021.100035

Matsuzaka, Y., & Yashiro, R. (2023). AI-Based Computer Vision Techniques and Expert Systems. Ai, 4(1), 289–302. https://doi.org/10.3390/ai4010013

Muslika, Irianti, A., & Sulfayanti. (2022). Klasifikasi Makanan Tradisional Mandar Menggunakan Ekstraksi Fitur Warna Dan Tekstur Dengan Metode K-Nearest Neighbour. Prosiding Seminar Nasional Energi, Kelistrikan, Teknik dan Informatika, 3, (pp. 60).

Nahak, H. M. I. (2019). Upaya Melestarikan Budaya Indonesia Di Era Globalisasi. Jurnal Sosiologi Nusantara, 5(1), 65–76. https://doi.org/10.33369/jsn.5.1.65-76

Putri, R. D., Tiurma, & Himawan. (2021). Traditional Food Serves In Wedding Ceremony As Cultural Heritage In Nagari Balahaie Kabupaten Padang Pariaman. Journal Food and Beverage, Product and Services, Accommodation Industry, Entertainment, 4(2), 1–18.

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 4510-4520).

Sarwinda, D., Argyadiva, T., Saragih, L. B. S., Oktareza, M., Handi Bagus, P., Fauzan, F., & Erickson, B. (2020). Automatic Multi-class Classification of Indonesian Traditional Food using Convolutional Neural Networks. 2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020, (pp. 43–47). https://doi.org/10.1109/IC2IE50715.2020.9274636

Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations (ICLR 2015), (pp. 1-14).

Subhi, M. A., & Md. Ali, S. (2018). A Deep Convolutional Neural Network for Food Detection and Recognition. 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), (pp. 284–287). https://doi.org/10.1109/IECBES.2018.8626720

Vankdothu, R., Hameed, M. A., & Fatima, H. (2022). A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method. Computers and Electrical Engineering, 101, 107960. https://doi.org/10.1016/j.compeleceng.2022.107960

Waluyo, G. B., Sari, Y. A., & Rahayudi, B. (2021). Pengenalan Citra Makanan Kue Tradisional menggunakan Ekstraksi Fitur HSV Color Moment dan Local Binary Pattern dengan KNearest Neighbour. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 5(12), 5641–5649.

Zhu, L., Spachos, P., Pensini, E., & Plataniotis, K. N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science, 4(December 2020), 233–249. https://doi.org/10.1016/j.crfs.2021.03.009




DOI: https://doi.org/10.26760/elkomika.v11i4.945

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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