ABSTRACT
The COVID-19 virus pandemic (Coronavirus Disease 19) has become a hot topic of conversation due to this date. A disease that attacks the human respiratory system becomes a case of the spread of the disease that is increasing daily. The method for detecting the movement of the human chest usually uses a belt-shaped device attached to the chest to see the respiratory rate. However, chest-mounted use requires contact with other people and promotes less privacy and comfort due to such attachments. Radar systems are urgently needed as contactless devices to reduce the risk of spreading disease. The use of this radar is a Frequency Modulated Continuous Wave (FMCW) technique that can perform semi-real-time monitoring. A monitoring system designed to perform small calculations to detect small movements in chest breathing. This FMCW radar system research compares the RPM radar with manual calculations to get an error value of less than 5%. The results of testing the respiratory target dataset with radar detection obtained an average error value of 1.68%. The proposed research is aimed at the health sector on vital signs. © 2022 IEEE.
ABSTRACT
The covid-19 pandemic has been pushing the development of online learning systems in Indonesia. In online learning, computer-based essay tests and assessments have an essential role. Essay test systems are designed to mimic the concept of essay tests without being computer-based. The answer from the lecturer is compared to the response from the student. The TF-IDF (Term Frequency -Inverse Document Frequency) cosine similarity is used. It is one of the methods of information re-gathering systems. The process in this model consists of two types: 1) creating a corpus/ inverted file, and the second is cosine similarity (CS) for calculating the similarity of the user's answers with the lecturer's. Creating a corpus/inverted file involves several stages like data collection, parsing sentences into terms, stoplist, weighting with IDF, and term weighting using TF-IDF. The cosine similarity process consists of parsing users' answers, weighting users' answers using TF-IDF, and finding cosine similarity values of users' answers with lecturers' answers using the vector space model. The highest cosine similarity value is taken to give the user's answer points. Testing the Essay Test system produces excellent grades. The tests were done Mean Squared Error (MSE) values resulted in an average MSE value of 3.28 from three students. © 2022 IEEE.
ABSTRACT
Data forecasting methods are essential in the business world to determine the company's future steps. However, the COVID-19 pandemic has hit the tourism economy hard, resulting in a slump in income. In this study, trials were conducted to analyze the reliability of forecasting methods on data affected by the COVID-19 pandemic. The method used is the Triple Exponential Smoothing method involving two models, namely Additive and Multiplicative. In this paper, the test is carried out using actual data derived from data from a service company engaged in tourist crossing transportation. Each method's alpha, beta, and gamma values are determined based on the parameters that produce the smallest error value. The experiment results show the predictability of the Triple Exponential Smoothing method by measuring the prediction error value based on the Mean Absolute Percentage Error (MAPE) value, which was 7.56% in the Additive model and 10.32% in the Multiplicative model before the pandemic happened. However, both methods' prediction measurements during a pandemic produce poor forecasts with an error percentage above 40%. Meanwhile, during the decline in pandemic cases, the value of the Triple Exponential Smoothing Multiplicative method was closer to the actual data with a prediction error value of 33.02%. Therefore, the Triple Exponential Smoothing Multiplicative method is more resistant and suitable for implementing into a forecasting system with actual data that influences pandemic events. © 2022 IEEE.
ABSTRACT
COVID-19 is a global pandemic afflicting our society. We propose covIoT, a novel Arduino-based automatic hand sanitizer dispenser, integrated with an oximeter, a heart rate monitor, a non-contact body temperature sensor, and voice assistant feedback. This system can be deployed as an end-to-end COVID patient monitoring system and also for automated sanitization. The system was tested on 100 people to evaluate its performance. The mean absolute error and root mean square error values were found to be 0.79 and 1.03 for the oximeter, 1.22 and 0.70 for the heart rate monitor and 1.07 and 1.28 for the body temperature monitor, respectively, compared to the industry-standard devices. These low error values indicate the high accuracy of our proposed system. We believe this is the first low-cost integrated patient monitoring and sanitization system with vocal feedback, to increase accessibility and ultimately helps combat the virus. © 2022 IEEE.