<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Application of Bidirectional Encoder Representations from \r\nTransformers (BERT) in Sentiment Analysis of COVID-19 \r\nBooster Vaccine"^^ . "Application of Bidirectional Encoder Representations from \r\nTransformers (BERT) in Sentiment Analysis of COVID-19 \r\nBooster Vaccine\r\nFaizal Ariansyah1\r\n, Sri Setyaningsih2\r\n, Boldson Herdianto Situmorang3 *\r\n1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan\r\nUniversity, Bogor, West Java, 16143, Indonesia\r\nAbstract\r\nCovid-19 was a huge outbreak across the world, including Indonesia, where it affected people in every \r\naspect of life. To minimize the death toll and reduce the infection rates, WHO strongly suggests the \r\ngovernment start running Covid-19 vaccines. Not only that but also the injection of booster vaccines is \r\nrequired to help boost people’s immunity in facing Covid-19 viruses that have been regularly mutated. \r\nThe requirement of having booster vaccines resulted in many pros and cons by the public. To understand \r\nthe perspective coming from Indonesian, this study carried out sentiment analysis of public response to \r\nCovid-19 booster through Twitter or X. Sentiment analysis in this study used the Knowledge Discovery \r\nin Database (KDD) algorithm and Bidirectional Encoder Representations from Transformers (BERT) as \r\nmachine learning for processing classification and modeling tweet data. This research revealed good fit \r\ngraphic loss results with a research accuracy of 85% based on a confusion matrix from 80% of training \r\ndata and 20% of test data among 1827 data tweets. Topic modeling is divided into positive topics which \r\ninclude the topics of discipline, facilities, effectiveness and achievement meanwhile negative topics \r\nwhich include the topics of side effects, worry and loss of confidence. All authors contributed equally to \r\nthis study.\r\nKeywords: BERT; booster; sentiment analysis; topic modeling; twitter"^^ . "2024-02-06" . . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Pakuan"^^ . . . . . . . . . . . . . . . . . . "Sri"^^ . "Setyaningsih"^^ . "Sri Setyaningsih"^^ . . "Boldson"^^ . "Herdianto Situmorang"^^ . "Boldson Herdianto Situmorang"^^ . . "Faizal"^^ . "Ariansyah"^^ . "Faizal Ariansyah"^^ . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Ilmu Pengetahuan Alam"^^ . . . "Program Studi Ilmu Komputer"^^ . . . . . . . "Application of Bidirectional Encoder Representations from \r\nTransformers (BERT) in Sentiment Analysis of COVID-19 \r\nBooster Vaccine (Text)"^^ . . . "Skripsi_065118260_Faizal Ariansyah.pdf"^^ . . "HTML Summary of #7678 \n\nApplication of Bidirectional Encoder Representations from \nTransformers (BERT) in Sentiment Analysis of COVID-19 \nBooster Vaccine\n\n" . "text/html" . . . "Ilmu Komputer"@en . .