Self-Isolation Monitoring of COVID-19 Patients Using Fuzzy Inference System-Tsukamoto

Abstract

In self-isolation of Covid-19 patients, it is very important to carry out regular condition checks. Currently, the examination of severity of patien’s condition can be carried out by the patient himself online with the tools as measurement provided by public health center, and the data can be monitored by medic team. Several applications for monitoring the daily condition of Covid-19 patients have been developed but the parameters used in the monitoring application are not standardized and the accuracy of the application is unknown. This study aims to develop a Covid-19 patient monitoring application using more complete and accurate parameters. The input parameters used are body temperature, O2 saturation, pulse rate, and respiratory rate. The output is the level of the Covid-19 patient's condition which is divided into mild, moderate, and severe, as well as information on the actions that must be taken. This research uses the Fuzzy Inference System-Tsukamoto method. The test results between the system output and expert testing related to the condition of Covid-19 patients show that this self-checking application for monitoring has an accuracy of 95%.

Fuzzy Logic Controller Design for Smart Watering System of Rose Cultivation

“…We used center weighted average method in defuzzification process, which involves taking a weighted sum of the output values based on their membership values. The weights are determined by the membership values of each fuzzy value, as shown in Equation ( 3) [14]. numerator calculates the weighted sum of these fuzzy outputs, while the denominator normalizes the result by dividing by the total weight.…”