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Tourism is one of the sectors that have been hit hard during the current Covid-19 pandemic. However, in 2021 the tourism sector is slowly rising marked by the number of tourist attractions that are starting to open to the public with the implementation of strict health protocols. However, in practice there are still many cases of violations of health protocols in various tourist attractions, ranging from the undisciplined use of masks, not keeping a distance (social distancing), and the absence of setting the ideal number of visitors during a pandemic like the current one. Managers of tourist attractions tend to ignore restrictions on the number of visitors due to the absence of an management system to run. There needs to be an application that can inform the condition of visitors to tourist attractions in real-time so that people have self-awareness of their personal health so that people can make early plans before traveling. In good governance, this application contributes to controlling the risk of Covid-19 transmission originating from crowded centers (tourist spots). The government can easily monitor the distribution of tourist visitor density in the area so that it can then be used as a basis for carrying out policies and handling in the field.
This study proposes the development of an application platform (software and hardware) monitoring the density of visitors to tourist attractions using the concept of object detection based on image processing and deep learning. The integration of hardware with web-based software will provide information on the density of visitors to tourist attractions that are easily accessible to the public so that they are expected to contribute to the handling of the current Covid-19 pandemic.
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