| dc.contributor.author | Saputri, Theresia Ratih Dewi | |
| dc.contributor.author | Lestari, Caecilia Citra | |
| dc.contributor.author | Siahaan, Salmon Charles | |
| dc.date.accessioned | 2023-08-18T08:46:19Z | |
| dc.date.available | 2023-08-18T08:46:19Z | |
| dc.date.issued | 2022-11 | |
| dc.identifier.issn | 25798901 | |
| dc.identifier.uri | http://dspace.uc.ac.id/handle/123456789/6569 | |
| dc.description | FOCUS AND SCOPE
JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research:
Software engineering
Artificial Intelligence
Data Mining
Computer network
Multimedia
Management Information System
Digital forensics
Game
etc
COPYRIGHT NOTICE
Creative Commons License
JUITA: Jurnal Informatika is licensed under a Creative Commons Attribution 4.0 International License. | en_US |
| dc.description.abstract | Abstract
To end the COVID-19 pandemics, the government attempted to accelerate the vaccination through various programs and collaboration. Unfortunately, the number is still relatively small compared to the number of populations in Indonesia. There are some reasons attributed to this challenge, one of them being the reluctance of citizens to accept the COVID-19 vaccine due to various factors. Knowing this factor to increase public compliance, the vaccination program can be speed-up. Unfortunately, traditionally acquiring the knowledge related to COVID-19 vaccine rejection can be challenging. One of the ways to capture the knowledge is by conducting a survey or interview related to COVID-19 vaccine acceptance. This method can be inefficient in terms of cost and resources. To address those problem, we propose a novel method for analyzing the topics related to the COVID-19 Indonesians’ opinions on Twitter by implementing topic modeling algorithm called Latent Dirichlet Allocation. We gathered more than 22000 tweets related to the COVID-19 vaccine. By applying the algorithm to the collected dataset, we can capture the what is general opinion and topic when people discuss about COVID-19 vaccine. The result was validated using the labeled dataset that have been gathered in the previous research. Once we have the important term, the strategy based on can be determined by the medical professional who are responsible to administer the COVID-19 vaccine. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto | en_US |
| dc.subject | Covid-19 | en_US |
| dc.subject | Vaccine | en_US |
| dc.subject | Topic Modeling | en_US |
| dc.subject | LDA | en_US |
| dc.title | JUITA : Jurnal Informatika | en_US |
| dc.title.alternative | WHAT DO INDONESIANS TALK WHEN THEY TALK ABOUT COVID-19 VACCINE: A TOPIC MODELING APPROACH WITH LDA | en_US |
| dc.type | Article | en_US |
