Effect of Digital Literacy and IT Service Quality on Tourists' Visit Decision to Marine Attractions During the Covid-19 Pandemic

This study aims to determine the effect of the level of Digital Literacy and the quality of Information Technology on Tourists' Visit Decisions to Lampung marine tourism during the COVID-19 Pandemic. From the results of questionnaires distributed to 106 tourists. All the questions in the given questionnaire were declared 100% valid by comparing the rcount value of each question item, which was more than the rtable value 0.195. The calculation results of the reliability test, all variables X1, X2, and Y have Cronbach's alpha coefficient of more than 0.6. So that all variables are declared reliable. Furthermore, based on the hypotheses testing of H1, H2, and H3, the results can be concluded that Digital Literacy and IT Service Quality individually or simultaneously positively and significantly influence the Tourist Decision to Visit Lampung Marine Attraction during the Covid-19 pandemic. Moreover, the R2 value of 0.631 indicates that the level of Digital Literacy and the IT Service Quality can explain Interest in Decisions to Visit Marine Tourism in Lampung during the COVID-19 pandemic at a percentage of 63.1%. © 2023 IEEE.

Crowd Counting during a Pandemic to Find Out Community Response to Activity Restriction Policy Using Deep Learning

The pandemic has occurred globally, especially in Indonesia since March 2019. It has been almost 2 years since the danger of spreading the COVID-19 virus was still lurking. Every effort has been made by the government by providing information, counseling, vaccines, and even regulations that limit the level of crowds to a certain scale according to the level of COVID-19 sufferers for each region. Currently, in early 2022, community activities in the city of Malang are allowed to meet face-to-face, such as in the education or office sector. However, if you look at the streets of the Malang city area, you can still see that people are sometimes careless because of the declining death graph due to COVID19. In fact, the government still urges the public to continue implementing the health protocol in various activities and limits the scale of the crowd on certain days. The researcher observes the public's response to all regulations given by the government by detecting objects and crowd counting at two points in the Malang city area, namely Jalan Soekarno Hatta and the entrance to the Malang toll road. The input in the form of photos of street crowds will be processed using computer vision and deep learning to identify the type of object for later analysis of the calculation results according to the calendar and the level of restrictions on current community activities. The percentage of object detection accuracy using deep learning is 80% using a confidence threshold value of 0.3. © 2022 IEEE.

Analysis of US Covid-19 Twitter Data Social Interest and Topic Changes

In this research, we analyzed the Covid-19 phenomena in the USA through analysis of Twitter data related to the Covid-19 pandemic in USA. We made this analysis with Twitter data from April and May of the year 2020. What we did differently in this research was focusing on one hashtag only so that we could focus on a fixed community. Our goal is to see if there is a connection or a pattern that could be found between the different output measures and plots. To do this, we focused on the country of the USA as a use-case. The difference in this analysis is that we didn't create our dataset by downloading data generally related to Covid-19 in the USA (from multiple tags), but rather we tracked one Twitter hashtag, ensuring that we track a certain group of the population so we could be sure about our population interest calculation results. © 2021 IEEE.