Identification of Indonesian Traditional Foods Using Machine Learning and Supported by Segmentation Methods - Rangkuti, Abdul Haris; Kerta, Johan Muliadi; Mogot, Roderik Yohanes; Athala, Varyl Hasbi (JOIV : International Journal on Informatics Visualization, 2024)
Traditional food is essential in preserving cultural heritage and is a vital part of Indonesian cuisine. In this research, we implement a methodology to identify the traditional Indonesian food using machine learning algorithms supported by various segmentation methods. This research aims to provide an efficient and accurate approach to classifying traditional foods, which can contribute to promoting and preserving Indonesia's culinary heritage. To conduct this research, we conducted experiments on 34 types of conventional Indonesian food originating from various provinces in Indonesia. The analysis of food images involved several segmentation algorithms, including Sobel, Prewitt, Robert, Scharr, and Canny filters. After the segmentation process, we proceeded with feature extraction and classification using traditional machine learning algorithms such as the Random Forest algorithm, Decision Tree, and derivatives of the SVM algorithm. These algorithms aimed to recognize the 34 types of traditional food. After conducting several experiments, we found that Random Forest with Robert's segmentation method was the highest-performance algorithm. It produced extraordinarily accurate results on the test dataset, with an accuracy performance of 85.52%, recall of 84.63%, precision of 83.77%, and an f1 score of 82.49%. Additionally, the best-performing algorithms with execution time averaged less than 1 minute. Another experimental result showed that the Random Forest algorithm with the Canny operator achieved an accuracy of 81.51%, recall of 84.97%, precision of 86.8%, and an f1 score of 85.61% on the test dataset. Furthermore, the Random Forest algorithm with the Sobel operator achieved accuracy results of 78.4%, recall of 65.3%, precision of 62.3%, and an f1 score of 63.71%.  In the SVM algorithms derivative, the Sigmoid SVM combined with the Scharr operator achieved the highest performance in its category across all classification metrics. In conclusion, this research offers valuable insights into classifying traditional Indonesian dishes using traditional machine learning algorithms. Simultaneously, this research aims to promote the appropriate and effective preservation and recognition of traditional Indonesian food.