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dc.date.accessioned2024-07-30T05:12:26Z
dc.date.available2024-07-30T05:12:26Z
dc.date.issued2024-07-11
dc.identifier.urihttps://library.universitaspertamina.ac.id//xmlui/handle/123456789/11902
dc.description.abstractThe Indonesian government is encouraging the transition to electric vehicles to reduce the use of fossil fuels and the negative environmental impact. However, this transition sparked controversy because Indonesia is still heavily dependent on coal-fired power plants, and many argue that the transition is not ready without adequate renewable energy and supporting infrastructure. Social aspects and public acceptance are key to accelerating the adoption of electric vehicles. Public opinion analysis is crucial in considering the introduction of electric vehicles in Indonesia due to the controversial nature of the transition. The opinion is transmitted through YouTube by taking comment data, then grouped into a topic to identify public opinion. The topic modeling method used is a BERTopic transformer model using IndoBERTweet in embedding. Once public opinion is modeled into a topic, changes in public opinion are evaluated using coherence score metrics and topic diversity as a measure of the consistency and diversity of the topic. Public opinion is also being analyzed to see changes in discussion topics for each year. The results show that in 2021, 2022, 2023, and 2024 the dominant public gives opinions regarding the environmental impact, the challenges of infrastructure and supporting technology development, considering the economic issues of market development, as well as the government's challenges in developing national development of electric vehicles in Indonesia. The resulting topics have a coherence value of around 0.6 to 1 and a diversity value of 0.95838. This indicates that the resulting themes have strong semantic similarities and high diversity in terms of word usage and capture various aspects of text documents well.en_US
dc.language.isootheren_US
dc.subjectBERTopic, Coherence Score, Electric Vehicles, Public Opinion, Topic Modelingen_US
dc.titleAnalisis Perubahan Opini Publik Terhadap Kendaraan Listrik Dari Data Komentar YouTube: Pemodelan Topik Menggunakan BERTopicen_US
dc.typeThesisen_US


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