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Indonesia is a culturally rich nation with more than three hundred ethnic groups. This sheer number of ethnic groups reflects the country’s diverse culture. One of the identities that could be associated with a group of people is its cuisine. As with the high number of ethnic groups, the diversity of Indonesian traditional food is also very high. However, the diversity of food is threatened by the current food systems, which could endanger food security of a population. To prevent this issue, a traditional food database system is created to monitor the food systems of each area in Indonesia. In this research, automatic traditional food classification is developed as one of the main features of this system. There were 17 Indonesian traditional foods from the Java area that were acquired and used as a dataset for this research. Several key features of the food dataset were extracted using various methods. The data were then classified using various machine learning algorithms. From the experiment, Random Forest classifier achieved the highest accuracy compared to other classical machine learning methods.

Traditional food knowledge (TFK) is an essential aspect of human life. In terms of sociocultural aspects, TFK is necessary to protect ancestral culture. In terms of health, traditional foods contain better and more natural ingredients compared to the ingredients of processed foods. Considering this background, in this study, data acquisition and automatic food recognition were performed for traditional food in Indonesia. The food images were captured in a professional mini studio. The food image data were captured under the same light intensity, camera settings, and shooting distance from the camera. The parameters were precisely measured and configured with a light intensity meter, adjustable lighting, and a laser distance measurement device. The data of 1644 traditional food images were successfully obtained in the data acquisition process. These images corresponded to 34 types of traditional foods, and 30–50 images were obtained for each type of food. The size of the raw food image data was 53 GB. The data were divided into sets for training, testing, and validation. An automatic recognition system was developed to classify the traditional food of Indonesia. Training was performed using several types of convolutional neural network (CNN) models such as Densenet121, Resnet50, InceptionV3, and Nasnetmobile. The evaluation results indicated that when using a high quality dataset, the automatic recognition system could realize satisfactory area under the receiver operating characteristics (AUROC) and high accuracy, precision, and recall values of more than 0.95.

Indonesia is a country rich in culture. One of Indonesia's culturaldiversity is on traditional foods. Traditional food not only has a role in the cultural aspect, but also has an influence on biodiversity. Unfortunately, the current diet of people endangers the existence of traditional foods, which indirectly will also affect Indonesia's food security. Indonesia Local Food Database is one solution proposed to prevent this problem, where the database will play a role to monitor food systems in Indonesia. In this research, database development will focus on collecting data for Sumatra traditionalfood, and also building a model for image classification which will later become one of the main features of the database. Some features like color and texture are extracted from the image. These features are used for classification using 5 classical machine learning models. Evaluation results show performance that as good as deep learning approach.