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All Journal Jurnal Teknologi Komputer dan Sistem Informasi JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Khoiril Hikmah
Program Studi Ilmu Komputer, Universitas Nahdlatul Ulama Blitar, Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Sentiment Analysis of Vaccine Booster during Covid-19: Indonesian Netizen Perspective Based on Twitter Dataset Khoiril Hikmah; Abd. Charis Fauzan; Harliana Harliana
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 5, No 2 (2022): JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Publisher : JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)

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Abstract

Corona virus has become a global threat at the end of 2019. The spread of this corona virus is very fast to all countries in the world.  The World Health Organization (WHO) has determined the status of the corona virus as a global pandemic called the Corona Virus Desease 2019 (Covid-19). Indonesia was detected first case of Covid-19 on March 2, 2020. After that, the number of Covid-19 cases in Indonesia increased every day and had a real impact on various sectors sectors, such as the economic and education. The Indonesian government has handled this health disaster, one of way that has been done is by holding a COVID-19 vaccine. Includes one dose vaccine, second dose vaccine and vaccine booster. The existence of this vaccine booster has received various opinions from Indonesian netizens who were conveyed through social media of Twitter. Therefore, this research aims to analyze the sentiments of Indonesian netizens about booster vaccination. In this study, data was collected from the Twitter dataset by crawling using the Rapidminer. Then, the data preprocessing stage is carried out consisting of: case folding, tokenizing, filtering and stemming. Sentiment classification is divided into positive sentiment, negative sentiment and neutral sentiment. Sentiment classification resolved using the Naive Bayes algorithm. This research resulted sentiment of vaccine booster during Covid-19 based on Indonesian netizen, include tweets of 23% positive sentiment, tweets of 15% neutral sentiment and tweets of 76% negative sentiment with an accuracy rate of 89%.
Sentiment Analysis of Vaccine Booster during Covid-19: Indonesian Netizen Perspective Based on Twitter Dataset Khoiril Hikmah; Abd. Charis Fauzan; Harliana Harliana
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 5, No 2 (2022): JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jtksi.v5i2.1161

Abstract

Corona virus has become a global threat at the end of 2019. The spread of this corona virus is very fast to all countries in the world.  The World Health Organization (WHO) has determined the status of the corona virus as a global pandemic called the Corona Virus Desease 2019 (Covid-19). Indonesia was detected first case of Covid-19 on March 2, 2020. After that, the number of Covid-19 cases in Indonesia increased every day and had a real impact on various sectors sectors, such as the economic and education. The Indonesian government has handled this health disaster, one of way that has been done is by holding a COVID-19 vaccine. Includes one dose vaccine, second dose vaccine and vaccine booster. The existence of this vaccine booster has received various opinions from Indonesian netizens who were conveyed through social media of Twitter. Therefore, this research aims to analyze the sentiments of Indonesian netizens about booster vaccination. In this study, data was collected from the Twitter dataset by crawling using the Rapidminer. Then, the data preprocessing stage is carried out consisting of: case folding, tokenizing, filtering and stemming. Sentiment classification is divided into positive sentiment, negative sentiment and neutral sentiment. Sentiment classification resolved using the Naive Bayes algorithm. This research resulted sentiment of vaccine booster during Covid-19 based on Indonesian netizen, include tweets of 23% positive sentiment, tweets of 15% neutral sentiment and tweets of 76% negative sentiment with an accuracy rate of 89%.
Co-Authors Abd. Charis Fauzan Harliana Harliana