Sentiment Analysis of YouTube Users on Blackpink Kpop Group Using IndoBERT

Slamet Riyadi; Lathifah Khansa Salsabila; Cahya Damarjati; Rohana Abdul Karim

doi:10.29407/intensif.v8i2.22678

Authors

Slamet Riyadi Universitas Muhammadiyah Yogyakarta https://orcid.org/0000-0003-1981-8876
Lathifah Khansa Salsabila Universitas Muhammadiyah Yogyakarta https://orcid.org/0009-0009-0480-8078
Cahya Damarjati Universitas Muhammadiyah Yogyakarta https://orcid.org/0000-0003-4389-6321
Rohana Abdul Karim Universiti Malaysia Pahang Al-Sultan Abdullah https://orcid.org/0000-0002-4469-912X

DOI:

https://doi.org/10.29407/intensif.v8i2.22678

Keywords:

Sentiment Analysis, Blackpink, IndoBERT, Youtube, K-Pop

Abstract

Background: The Korean Pop (K-Pop) phenomenon has become an important part of popular culture worldwide, with Blackpink being one of the most influential groups. Analyzing sentiment toward Blackpink is urgent, given its growing popularity and wide influence among fans worldwide. In the present technological era, social media platforms such as YouTube have evolved into a space where artists and their fans may interact with each other. As a consequence, social media has become a powerful tool for assessing the emotional tone and sentiment conveyed by individuals. Objective: This research aims to explore the trend of public sentiment towards Blackpink and evaluate how well the IndoBERT model analyzes the sentiment of Indonesian texts. Methods: The objective of this study is to examine the pattern of public sentiment towards Blackpink and assess the proficiency of the IndoBERT model in analyzing the sentiment of Indonesian writings. Results: The findings demonstrated that the IndoBERT model had an exceptional level of precision, achieving a 98% accuracy rate. In addition, it obtained a f1, recall, and accuracy score of 95%. The remarkable results demonstrate the efficacy of the IndosBERT technique in evaluating the emotion of Indonesian-language literature towards Blackpink. Conclusion: This study enhances the knowledge of how fans and audiences react to K-pop material and establishes a foundation for future research and advancement. The impressive precision of the IndoBERT model showcases its capacity for sentiment analysis in Indonesian literature, making it a useful tool for future research endeavors.

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Author Biographies

Slamet Riyadi, Universitas Muhammadiyah Yogyakarta

Department of Information Technology, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta

Lathifah Khansa Salsabila, Universitas Muhammadiyah Yogyakarta

Department of Information Technology, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta

Cahya Damarjati, Universitas Muhammadiyah Yogyakarta

Department of Information Technology, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta

Rohana Abdul Karim, Universiti Malaysia Pahang Al-Sultan Abdullah

Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah

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