Optimasi Kontras Dan Ketajaman Citra Pada Pengenalan Makanan Indonesia Berbasis Machine Learning
DOI:
https://doi.org/10.33557/g9afej42Keywords:
indonesian food identification, preprocessing, ResNet50, transfer learningAbstract
Indonesia has a rich culinary diversity, encompassing various types of food from different regions. In the current era of technological advancement, the application of artificial intelligence has grown significantly across multiple sectors, including in the identification of Indonesian food images. This research provides the impact of various image preprocessing techniques on the AI-based food identification system. The preprocessing methods examined include Zero Component Analysis (ZCA), Histogram Equalization (HE), Contrast Stretching, and Image Sharpening. The evaluation of these preprocessing methods was conducted to determine which technique provides the best performance in assisting the identification of Indonesian food using a Convolutional Neural Network (CNN) with a ResNet-50 transfer learning model. Performance measurement was carried out using a confusion matrix by calculating Accuracy, precision, recall, and F1-score. The results of this research show that the use of the Image Sharpening method yields higher accuracy and precision on the testing data compared to other methods, those are 0.9748 and 0.98, respectively. Next, a high level of accuracy was also demonstrated by the Contrast Stretching method, with an accuracy score of 0.9712.
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[1] S. Upadhyay and G. Goel, “Food computing research opportunities using AI and ML,” in Image based computing for food and health analytics: Requirements, challenges, solutions and practices: IBCFHA, Springer, 2023, pp. 1–23.
[2] B. Shah and H. Bhavsar, “Overview of deep learning in food image classification for dietary assessment system,” in Intelligent Systems, Technologies and Applications: Proceedings of Sixth ISTA 2020, India, Springer, 2021, pp. 265–285.
[3] T. Karlita, B. P. Afif, and I. Prasetyaningrum, “Indonesian Traditional Cake Classification Using Convolutional Neural Networks,” in International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021), Atlantis Press, 2022, pp. 924–929.
[4] C. Mahaputri, Y. Kristian, and E. Setyati, “Pengenalan Makanan Tradisional Indonesia Beserta Bahan-bahannya dengan Memanfaatkan DCNN Transfer Learning,” INSYST: Journal of Intelligent System and Computation, vol. 4, no. 2, pp. 61–68, 2022.
[5] H. Singh, Practical machine learning and image processing: for facial recognition, object detection, and pattern recognition using Python. Springer, 2019.
[6] P. Misra and A. S. Yadav, “Impact of preprocessing methods on healthcare predictions,” in Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), 2019.
[7] K. R. Ummah, T. Karlita, R. Sigit, E. M. Yuniarno, I. K. E. Purnama, and M. H. Purnomo, “Effect of image pre-processing method on convolutional neural network classification of COVID-19 CT scan images,” International Journal of Innovative Computing, Information and Control, vol. 18, no. 6, pp. 1895–1912, 2022.
[8] K. K. Pal and K. S. Sudeep, “Preprocessing for image classification by convolutional neural networks,” in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE, 2016, pp. 1778–1781.
[9] F. Marpaung, Z. A. Koemadji, M. Hidayat, A. Widianto, and F. Aulia, “Selection of Food Identification System Features Using Convolutional Neural Network (CNN) Method,” Scientific Journal of Informatics, vol. 10, no. 2, pp. 205–2016, 2023.
[10] J. Chaki and N. Dey, A beginner’s guide to image preprocessing techniques. CRC Press, 2018.
[11] A. Kalapos and B. Gyires-Tóth, “Whitening Consistently Improves Self-Supervised Learning,” in 2024 International Conference on Machine Learning and Applications (ICMLA), IEEE, 2024, pp. 448–453.
[12] M. M. Weli and O. M. Abdullah, “Digital Image Noise Reduction Based on Proposed Smoothing and Sharpening Filters,” The Indonesian Journal of Computer Science, vol. 13, no. 4, 2024.
[13] A. B. Panambur, P. Madhu, and A. Maier, “Effect of random histogram equalization on breast calcification analysis using deep learning,” in Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022, Springer, 2022, pp. 173–178.
[14] B. Hartono and V. Lusiana, “Analisa Teknik Adaptive Histogram Equalization dan Contrast Stretching untuk Perbaikan Kualitas Citra,” Dinamik, vol. 19, no. 1, 2014.
[15] A. Firma and S. Oktamuliani, “Pengolahan Filtering dan Contrast Enhancement untuk Meningkatkan Kualitas Resolusi Citra Ultrasonografi Abdomen,” JOURNAL ONLINE OF PHYSICS, vol. 8, no. 1, pp. 51–54, 2022.
[16] J. Wang and Y. Chen, Introduction to transfer learning: algorithms and practice. Springer Nature, 2023.
[17] D. A. Roberts, S. Yaida, and B. Hanin, The principles of deep learning theory, vol. 46. Cambridge University Press Cambridge, MA, USA, 2022.
[18] A. Artasanchez and P. Joshi, Artificial Intelligence with Python. Packt Publishing Birmingham, UK, 2020.
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