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The new life adaptation protocol implemented in Institut Teknologi Sumaterea (ITERA) during the COVID-19 pandemic requires visitor identity check to restrict access to several buildings and reduce the massive spread of the corona virus. The inspection is carried out by manually collecting visitor information, which can result in a queues buildup of vehicles that want to enter the ITERA environment, and also, the identity of incoming visitors is not adequately monitored. In addition, the smart camera system for visit recording is built with the ability to identify visitors' faces, which triggers automatic gate access and records visitor information. Visitor information is stored in the form of identity number, name, visitor status, time/date of visiting, and information about the location of visitors. The visitor data is stored in a database and can be accessed by ITERA on the visitor website page. The system has an ability to trigger automatic gate to give an order to open or close the gate based on the face recognition results. The system development is divided into three stages: face recognition development, website-based data visualization creation, and hardware development. At each stage, the development produces a system with face recognition capabilities, a visitor recorder website, and a series of automatic gate hardware consecutively. The face recognition system connects directly to the website via the Firebase database. The development of the system capability for face recognition is carried out using a learning model of the K-Nearest Neighbor (K-NN) algorithm method, which obtains the training results have an accuracy of 99% using a confusion matrix method. [ABSTRACT FROM AUTHOR]