ANALISIS SENTIMEN MEDIA SOSIAL X TERHADAP FILM DOKUMENTER “DIRTY VOTE” MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

BAHARI, MUHAMMAD DAFFA NIZAR and Suarli, Pacu Putra (2024) ANALISIS SENTIMEN MEDIA SOSIAL X TERHADAP FILM DOKUMENTER “DIRTY VOTE” MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

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Abstract

This research aims to analyze the sentiment of the documentary film "Dirty Vote" based on public comments on social media X using the Support Vector Machine (SVM) method. The movie "Dirty Vote" is a criticism of the implementation of elections and candidate pairs in Indonesia, thus triggering various reactions among the public. Public comments on social media X became a valuable source of information to understand the sentiment towards this movie. In this study, we implemented the SVM classification algorithm to classify comments as positive or negative sentiment. Comment data from social media X was collected and went through a text pre-processing stage, then relevant features were extracted to train the SVM model. The evaluation results show that the SVM method can recognize the sentiment of comments on the movie "Dirty Vote" with significant accuracy. The findings provide valuable insights for filmmakers, researchers, and related parties regarding the acceptance of the movie by the public. This research also confirms the potential use of the SVM method in sentiment analysis on comment data on social media, which can be applied in other contexts in the future.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Film Dokumenter, Support Vector Machine
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing T Technology > T Technology (General) > T57-57.97 Applied mathematics. Quantitative methods
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Muhammad Daffa Nizar Bahari
Date Deposited:	19 Jul 2024 06:39
Last Modified:	19 Jul 2024 06:39
URI:	http://repository.unsri.ac.id/id/eprint/151752

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