Identifikasi Opini Publik Terhadap Kendaraan Listrik dari Data Komentar YouTube: Pemodelan Topik Menggunakan BERTopic

Kristine Angelina Simanjuntak; Muhamad Koyimatu; Yolla Putri Ervanisari; Tasmi

doi:10.38204/tematik.v11i2.2096

Kristine Angelina Simanjuntak Universitas Pertamina
Muhamad Koyimatu Universitas Pertamina
Yolla Putri Ervanisari Universitas Pertamina
Tasmi Universitas Pertamina

DOI: https://doi.org/10.38204/tematik.v11i2.2096

Keywords: BERTopic, coherence score, electric vehicles, public opinion, topic modeling

Abstract

The Indonesian government is encouraging the transition to electric vehicles to reduce the use of fossil fuels and the negative environmental impact. 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. 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. 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.

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