THE USE OF A SCORECARD TO EVALUATE THE PUBLIC'S COMPREHENSION OF COVID-19: A PILOT STUDY

The rapid spread of information and infodemic might result in public confusion and hinder the handling of the COVID-19 pandemic. Public comprehension of COVID-19 as part of health literacy is an important determinant to filter hoaxes from facts. Therefore, a scoring card called the Karlivid (the COVID-19 literacy and public vaccination scorecard) was developed to evaluate the individual's comprehension level of COVID-19. A pilot study was conducted with this scoring card. The participants were recruited via consecutive random sampling by using emails from the researcher's contact list (n=92). A total of 78.3% of the respondents were considered to have an adequate comprehension level. Approximately 77% of all respondents agreed that this card could help them know their comprehension level, 81.5% agreed that this card could improve their comprehension, 81.5% agreed that the items in this card could help them screen facts from hoaxes, and 81.5% agreed that the language used was easily understood by the laypersons. Therefore, the Karlivid is a valid and reliable scorecard that can be used to evaluate public comprehension of COVID-19. Most of the respondents also had a good level of comprehension of this assigned topic. © 2022, Universitas Jenderal Soedirman. All rights reserved.

Performance Analysis of the Triple Exponential Smoothing Method during the Covid19 Pandemic on Tourist Visit Data

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.