Laporkan Masalah

Public Opinion on PeduliLindungi application on Twitter Period January 2021 � November 2021

ANANTA SATRIA W, Indri Dwi Apriliyanti, SIP, MBA, Ph.D.

2022 | Skripsi | S1 MANAJEMEN DAN KEBIJAKAN PUBLIK

The COVID-19 pandemic has spread to various regions around the world. Different countries have their ways of reducing the spread of the coronavirus. Taiwan, Singapore and South Korea are three countries that are considered capable of overcoming the early golden moment by applying technology that aims to find contact tracing between citizens. The Indonesian government issued a similar policy by launching the PeduliLindungi application. The application has the function of screening, ensuring a person's health data and checking health status. This study aims to determine public opinion on the PeduliLindungi application on Twitter social media and understand the relationship between public opinion on Twitter social media, media agenda and changes to the PeduliLindungi application. The research method used in this study is a hybrid method, which is a type of research that seeks to combine quantitative research and qualitative research. Quantitative research is carried out to quantify the number of chats on social media towards the PeduliLindungi application, while qualitative research is used to interpret the texts to understand a phenomenon. The data collection technique was carried out by text scraping on tweets related to the keywords 'PeduliLindungi' and #PeduliLindungi. The results of the study indicate that public opinion on the PeduliLindungi application on Twitter social media has negative sentiments because (1) the application still has errors, (2) the application does not work optimally, and (3) the available features cannot be appropriately maximized. The findings also show that the public opinion in Twitter became a massive public discussion, and that trending topics have the potential to become a media agenda that ultimately affects policy changes. This is proven by the similarities between the bug updates to the PeduliLindungi application and what was discussed on Twitter.

The COVID-19 pandemic has spread to various regions around the world. Different countries have their ways of reducing the spread of the coronavirus. Taiwan, Singapore and South Korea are three countries that are considered capable of overcoming the early golden moment by applying technology that aims to find contact tracing between citizens. The Indonesian government issued a similar policy by launching the PeduliLindungi application. The application has the function of screening, ensuring a person's health data and checking health status. This study aims to determine public opinion on the PeduliLindungi application on Twitter social media and understand the relationship between public opinion on Twitter social media, media agenda and changes to the PeduliLindungi application. The research method used in this study is a hybrid method, which is a type of research that seeks to combine quantitative research and qualitative research. Quantitative research is carried out to quantify the number of chats on social media towards the PeduliLindungi application, while qualitative research is used to interpret the texts to understand a phenomenon. The data collection technique was carried out by text scraping on tweets related to the keywords 'PeduliLindungi' and #PeduliLindungi. The results of the study indicate that public opinion on the PeduliLindungi application on Twitter social media has negative sentiments because (1) the application still has errors, (2) the application does not work optimally, and (3) the available features cannot be appropriately maximized. The findings also show that the public opinion in Twitter became a massive public discussion, and that trending topics have the potential to become a media agenda that ultimately affects policy changes. This is proven by the similarities between the bug updates to the PeduliLindungi application and what was discussed on Twitter.

Kata Kunci : Social media, Public Opinion, Big Data Analysis, PeduliLindungi

  1. S1-2022-415957- abstract.pdf  
  2. S1-2022-415957- bibliography.pdf  
  3. S1-2022-415957-tableofcontent.pdf  
  4. S1-2022-415957-title.pdf