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All Journal Teknik: Jurnal Ilmu Teknik dan Informatika INFORMATIKA J-ENSITEC (Journal of Engineering and Sustainable Technology)
Marvin Donald Richardo Aronggear
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : J-ENSITEC (Journal of Engineering and Sustainable Technology)

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ANALISIS SENTIMENT KOMENTAR VIDEO YOUTUBE “EPIC RAP BATTLE OF PRESIDENCY 2024” MENGGUNAKAN ALGORITMA NAÏVE BAYES & SVM Gavin Berylian Josepto; Rafif Dhia Yusrana; Marvin Donald Richardo Aronggear; Viktor Handrianus Pranatawijaya; Ressa Priskila
J-ENSITEC Vol. 10 No. 02 (2024): June 2024
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jensitec.v10i02.9210

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%.
Co-Authors Gavin Berylian Josepto Jadiaman Parhusip, Jadiaman Rafif Dhia Yusrana Ressa Priskila Thomas Zugildo Magnus Viktor Handrianus Pranatawijaya