ANALYSIS OF PUBLIC OPINION SENTIMENT TOWARDS THE KANJURUHAN MALANG TRAGEDY ON TWITTER SOCIAL MEDIA USING THE SUPPORT VECTOR MACHINE METHOD

Herlambang, Fahri Putra (2024) ANALYSIS OF PUBLIC OPINION SENTIMENT TOWARDS THE KANJURUHAN MALANG TRAGEDY ON TWITTER SOCIAL MEDIA USING THE SUPPORT VECTOR MACHINE METHOD. Tugas Akhir thesis, Informatics.

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Abstract

ABSTRACT The Kanjuruhan tragedy that occurred on October 1, 2022 is a dark record for the world of football and also the stadium security mitigation system in Indonesia. This tragedy killed as many as 125 people died, 21 people were seriously injured, and 304 people were slightly injured. The large number of victims raises the pros and cons of who should be responsible for this tragedy. According to DataIndonesia.id, Twitter social media users in Indonesia reached 18.45 million users per 2022, therefore Twitter makes social media suitable for carrying out sentiment analysis. Sentiment analysis on Twitter is the process of understanding, extracting and processing tweets automatically to obtain sentiment information contained in the Tweet. People's tweets related to the tragedy of kanjuruhan can be used to see a picture of public opinion on the topic of this tragedy of kanjuruhan. The large number of incoming tweets about the tragedy of kanjuruhan prompts the need for methods that help to see public opinion effectively. In this study, tweet sentiment classification was carried out using the Support Vector Machine method. This method will classify whether a tweet contains a positive or negative sentiment by finding the best hyperlane of the two classification classes. The results of this classification are expected to determine how the public opinion on who is responsible for the Kanjuruhan tragedy. The research was able to produce an accuracy value of 95.65% Keywords: Police Violence, Riots, Football, Supporters, Sentiment Analysis, Support Vector Machine

Item Type: Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir)
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Sains Dan Teknologi > S1 Informatika
Depositing User: Kaprodi S1 Informatika UTY
Date Deposited: 09 Aug 2024 01:24
Last Modified: 09 Aug 2024 01:24
URI: http://eprints.uty.ac.id/id/eprint/15935

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