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Abstract Traditional Food Knowledge (TFK) is needed to define the acculturation of culture, society, and health in the context of food. TFK is essential for a human’s cultural, economic, and health aspects. Variations of ethnicity, culture, and lifestyle affect the diversity of traditional Indonesian food. Recognition of food is needed to maintain the sustainability of traditional food. Nowadays, there are many food dataset collections, but there needs to be a dataset that specifically collects standard food datasets. Our main contributions to the TFK research field are professional food image data acquisition, innovative development of an automatic, scalable food recognition system, and multi-process inference service. There are 34 variations of traditional foods from all regions in Indonesia that were acquired in this dataset. The dataset comprises 1644 high-quality images captured by professional cameras and 1020 by a smartphone. Several deep learning models are implemented in food recognition systems. This system can accommodate the addition and reduction of food variations in the knowledge recognition system and is capable of multiple concurrent requests at a time. The current prototype incorporates traditional types of food from Indonesia. However, the food model can also be expanded to other countries traditional foods. The automatic recognition systems are evaluated using several deep-learning network models. The experiment results have shown that the AUROC score is 0.99, and the request success rate can be improved by 70% with a multiprocess inference service.

Smart cities are faced with the challenges of systematically initiating the strategies needed to implement predetermined goals according to their plan. Therefore, this study aims to identify smart cities components and build an initial model that can be used for smart cities initiatives in Indonesia. The systematic review methods were used to overview research trends, architecture, components, and challenges to build an initial model for Indonesia. The systematic review results show popular topics, such as the standardization of a smart city and the strategies used to determine relevant models in each city's uniqueness and context. The results also identified thirteen smart city components and their challenges. Furthermore, this study's novelty is proposed the smart city's initial models with the smart government as a key component and to be a centre of other smart city components. The triangulation method determined the research validation using systematic review finding, expert judgment, and a questionnaire for collecting random data from 66 respondents from various cities in Indonesia. The collected survey data and initial models were validated by expert judgment, which showed that the proposed smart city's initial model component's agreement is 90%. In future, these results may be used to develop an assessment in smart cities implementation.