Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner
The Coronavirus or other designations is COVID-19 (Corona Virus Disease) appeared in November 2019 in Wuhan, China. Over time, the virus is no longer categorized as an outbreak but is categorized as a pandemic or has spread to almost all countries in the world, including Indonesia. The emergence of...
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| Format: | Article |
| Language: | English |
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INTI International University
2023
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| Online Access: | http://eprints.intimal.edu.my/1783/ http://eprints.intimal.edu.my/1783/1/jods2023_08.pdf |
| _version_ | 1848766833647157248 |
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| author | Dayu, Wijaya Leon A., Abdillah |
| author_facet | Dayu, Wijaya Leon A., Abdillah |
| author_sort | Dayu, Wijaya |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | The Coronavirus or other designations is COVID-19 (Corona Virus Disease) appeared in November 2019 in Wuhan, China. Over time, the virus is no longer categorized as an outbreak but is categorized as a pandemic or has spread to almost all countries in the world, including Indonesia. The emergence of COVID-19 in Indonesia in February 2020 has resulted in many sectors experiencing losses, not only in health but also in the economic sector. Recently there was a new mutation to the COVID-19 Virus, namely Omicron. Omicron has been shown to be much more infectious than the other variants with an increased ability to evade vaccines and cause re-infection. This study aims to present a result of sentiment analysis on the new variant of the COVID-19 Virus, namely Omicron which is divided into three (three) classes: positive, negative, and neutral. Then, the comments will be manually labeled followed by classification using the Nave Bayes algorithm and RapidMiner software. This study's findings revealed that 84% of the community responded positively, 7% of the community responded Neutral and 9% of the community responded negatively. It can be concluded that the community responded positively to the issue of the latest variant of the COVID-19 Omicron virus because there is also the possibility that the contents of the latest Omicron COVID-19 virus may also be dangerous from the beginning of the emergence of the COVID-19 Virus in the world. |
| first_indexed | 2025-11-14T11:57:26Z |
| format | Article |
| id | intimal-1783 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:57:26Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-17832023-08-24T09:11:24Z http://eprints.intimal.edu.my/1783/ Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner Dayu, Wijaya Leon A., Abdillah QA75 Electronic computers. Computer science QA76 Computer software The Coronavirus or other designations is COVID-19 (Corona Virus Disease) appeared in November 2019 in Wuhan, China. Over time, the virus is no longer categorized as an outbreak but is categorized as a pandemic or has spread to almost all countries in the world, including Indonesia. The emergence of COVID-19 in Indonesia in February 2020 has resulted in many sectors experiencing losses, not only in health but also in the economic sector. Recently there was a new mutation to the COVID-19 Virus, namely Omicron. Omicron has been shown to be much more infectious than the other variants with an increased ability to evade vaccines and cause re-infection. This study aims to present a result of sentiment analysis on the new variant of the COVID-19 Virus, namely Omicron which is divided into three (three) classes: positive, negative, and neutral. Then, the comments will be manually labeled followed by classification using the Nave Bayes algorithm and RapidMiner software. This study's findings revealed that 84% of the community responded positively, 7% of the community responded Neutral and 9% of the community responded negatively. It can be concluded that the community responded positively to the issue of the latest variant of the COVID-19 Omicron virus because there is also the possibility that the contents of the latest Omicron COVID-19 virus may also be dangerous from the beginning of the emergence of the COVID-19 Virus in the world. INTI International University 2023 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1783/1/jods2023_08.pdf Dayu, Wijaya and Leon A., Abdillah (2023) Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner. Journal of Data Science, 2023 (08). pp. 1-11. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Dayu, Wijaya Leon A., Abdillah Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner |
| title | Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner |
| title_full | Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner |
| title_fullStr | Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner |
| title_full_unstemmed | Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner |
| title_short | Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner |
| title_sort | sentiment analysis of omicron covid-19 variant using naïve bayes classifier and rapidminer |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1783/ http://eprints.intimal.edu.my/1783/ http://eprints.intimal.edu.my/1783/1/jods2023_08.pdf |