ANALISIS SENTIMENT KOMENTAR VIDEO YOUTUBE “EPIC RAP BATTLE OF PRESIDENCY 2024” MENGGUNAKAN ALGORITMA NAÏVE BAYES & SVM

Authors

  • Gavin Berylian Josepto Universitas Palangka Raya
  • Rafif Dhia Yusrana Universitas Palangka Raya
  • Marvin Donald Richardo Aronggear Universitas Palangka Raya
  • Viktor Handrianus Pranatawijaya Universitas Palangka Raya
  • Ressa Priskila Universitas Palangka Raya

DOI:

https://doi.org/10.31949/jensitec.v10i02.9210

Keywords:

You Tube Maximum, rapfight, Epic Rap Battle of Presidency 2024, Naïve Bayes, Support Vector Machine(SVM)

Abstract

Sentiment analysis of YouTube video recordings has become important in this increasingly technologically advanced era to understand users' reactions and opinions on various topics, including politics. Rapfight videos, especially those related to the presidential election, are one type of content that often attracts attention. This research uses the Naïve Bayes algorithm to assess the sentiment of comments on the video "Epic Rap Battle of Presidency 2024" on the YouTube site. The algorithms used in this research are Naïve Bayes Classifier and Support Vector Machine (SVM). There are five processes in this research, namely collecting YouTube comment data, preprocessing, labeling, classification, implementation and testing. From 3650 comment data on YouTube regarding the video "Epic Rap Battle of Presidency 2024" based on the results of the analysis, it was found that 31% of the comments were positive, 7% of the comments were negative and 62% of the comments were neutral. The accuracy results using the Naïve Bayes Classifier algorithm were 84.82% and the accuracy results using the Support Vector Machine (SVM) algorithm got the best results at 93.3%.

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Published

2024-06-09

How to Cite

Gavin Berylian Josepto, Rafif Dhia Yusrana, Marvin Donald Richardo Aronggear, Viktor Handrianus Pranatawijaya, & Ressa Priskila. (2024). ANALISIS SENTIMENT KOMENTAR VIDEO YOUTUBE “EPIC RAP BATTLE OF PRESIDENCY 2024” MENGGUNAKAN ALGORITMA NAÏVE BAYES & SVM. J-ENSITEC (Journal of Engineering and Sustainable Technology), 10(02), 1064–1069. https://doi.org/10.31949/jensitec.v10i02.9210

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