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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]

Learning evaluation is needed to determine the success of implementing educational institutions. Assessment of the learning process can be in the form of a short answer test,an essay, or multiple choice. As the main parameter, the exam must have an assessment rubic and competitive questions and become a significant part of the learning benchmark. The test results must be valid and reliable to determine whether the learning system has been appropriately implemented. In the digital era, it is necessary to have a fast and efficient test and have accuracy in giving short answer scores or essays. Teacher educators and lecturers will have a value gap when correction is made. Gaps are detrimental to students when answering questions from exams that have been carried out. Using cosine similarity and dice similarity is one solution to evaluating student learning. Cosine and dice similarity will compare the students' answers with the answer key. The smaller the error value of the responses obtained, the participants' answers get a high accuracy value. The value of the small error given by the two algorithms, cosine similarity in the short answers to the student exams, receives a score of 21%. 16.21% error value generated by dice similarity in quick answers. [ABSTRACT FROM AUTHOR]