MACHINE LEARNING DETEKSI PROTOKOL KESEHATAN MEMASUKI RUANGAN BERBASIS INTERNET OF THINGS MENGGUNAKAN ALGORITMA K-MEANS - Usep Tatang Suryadi; Juliansyah Juliansyah; Aa Zezen Zaenal Abidin; Yuli Murdianingsih; Muhammad Faizal (STMIK SUBANG, 2022)
In the end of 2019, it was a bad year for the whole world, especially for the world of health, because of a malignant disease that attacks the human respiratory system which causes sore throat and dry cough. The disease is called Corona or we often hear about COVID-19. (Coronavirus Disease 2019). For preventive measures, everyone must implement health protocols including wearing a mask, keeping a safe distance, washing hands, and body temperature must be in the range of 36 to 37.02 degrees Celsius above 37.02 degrees Celsius, it is said to have a fever, fever indicates a problem in the body man. Checking human body temperature is currently still carried out conventionally involving two individuals, an officer and someone who will be checked for temperature, the distance between the officer and the person who will be checked for temperature is around 60 cm, this has violated the rules for keeping a distance where social distancing is ranging from 1 to 2 meters between individuals, departing from this problem the authors are interested in making a tool, namely Machine Learning Detection of ProKes Entering Rooms Based on Internet of Things Using the K-Means Algorithm on the Google Firebase Platform. The methodology used by the author includes literature study, documentation, data mining, system analysis, system design, system development, system testing, machine learning. went well as expected. This tool is able to properly detect masks and human body temperature in a non-contact manner. The data obtained by the tool can be analyzed using the K-means algorithm, showing that the K-means algorithm can work well, the results obtained in the 3rd iteration with the same ratio value as the previous iteration, namely 0.083743.