KURVATEK Vol. 7, No. 2, November 2022, pp. 133 - 142 e-ISSN: 2477-7870 p-ISSN: 2528-2670 ◼ 133 Received September 25, 2022; Revised October 10, 2022; Accepted November 16, 2022 PROTOTYPE OF MASK RECOGNITION AND BODY TEMPERATURE IN REAL TIME WITH AMG8833 THERMAL CAM SENSOR FOR COVID-19 EARLY WARNING BASED ON MINICOMPUTER Aan Burhanuddin 1 , Muchamad Malik 2* , Yuris Setyoadi 3 1,3 Department of Mechanical Engineering, Universitas PGRI Semarang, Indonesia 1 Email: aan.burhanuddin@gmail.com 3 Email: yurissetyoadi@upgris.ac.id 2 Department of Industrial Engineering, Universitas Proklamasi 45 Yogyakarta, Indonesia *Email corresponding: m.malik@up45.ac.id How to cite: A. Burhanuddin, M. Malik, and Y. Setyoadi, "Prototype of mask recognition and body temperature in real time with AMG8833 thermal cam sensor for Covid-19 early warning based on minicomputer," Kurvatek, vol. 7, no. 2, pp. 133 - 142, 2022. doi: 10.33579/krvtk.v7i2.3585 [Online]. Abstract — On April 19, 2020, the Republican Covid Task Force declared that the Covid-19 pandemic was a national disaster in Indonesia. At that time, it was confirmed that there were 6575 cases and an increase of 5.23% compared to the previous day, then there were 5307 people in treatment which increased by 5.55% compared to the previous day, it was reported that 582 people died, which increased by 8.79 % compared to the previous day, and 686 recovered patients. WHO reports that the case fatality rate (CFR) or the death rate of Covid-19 cases in Indonesia reached 8.3%, which is twice the world's CFR. In this study, the main focus is to detect masks and body temperature used by visitors with various variations of masks on the market today, and next is to control the servo motor according to the detection conditions whether using a mask in real-time. Based on research on the system that has been tested, it shows that the components used to generate heat are very effectively used and can work as expected, and the MobilenetV2 method applied to the Raspberry Pi as the brain of the system can work as expected and has an accuracy rate of 99%. The AMG8833 sensor can read effectively at a maximum distance of 30 cm, the temperature reading deviation level is 0.1⁰C. Keywords: Thermal Cam AMG8833, Mask Detection, MobilenetV2, Covid 19 I. INTRODUCTION In December 2019, the city of Wuhan, Hubei province, one of the largest cities in China with a population of 14 million, was suspected to be the center of a pneumonia outbreak with an unknown cause. After more than seven days on January 7, 2020, Chinese health authorities have confirmed that they have identified the strain of the new coronavirus (COVID-19). On January 30, 2020, the Director-General of WHO gave the final decision on the determination of a Public Health Emergency of International Concern (PHEIC), regarding the outbreak in China [1] – [3]. The 2019 Coronavirus Outbreak, later called COVID- 19, is an infectious disease caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [4]. Typical symptomatic cases of the novel coronavirus disease (COVID-19) that have been identified can cause sore throat, fever, muscle pains, and cough [5]. In conducting this research, it is very important for researchers to find out and look for reference sources from books, journals, and previous studies. It is done to avoid duplication and plagiarism and can be used as learning materials for researchers to find research novelties so that they continue to develop. Below are some literature studies that have been carried out by the author regarding previous research that has been done related to the themes and methods that will be used. The previous face detection and recognition approach was based on an artificial conditional network which classification had been trained on top of the identified facial identity data and then a bottleneck layer was used as an intermediate representation used as a generalization of recognition beyond the identity data used in the training. The weakness of this approach is it’s the indirectness and inefficiency [6]. Some other studies related to face recognition using PCA have been carried out, and this method is a linear transformation that can be easily studied in one layer of the network. The research is about "Automated Attendance System Using Face Recognition" which was carried out using the Local Binary Pattern Histogram (LBPH) and Histograms of Oriented Gradients (HOG) methods in face detection [7].