ANALYSIS OF COVID-19 VACCINATION SENTIMENT USING THE SUPPORT VECTOR MACHINE METHOD

MUBAROK, ALFAN (2024) ANALYSIS OF COVID-19 VACCINATION SENTIMENT USING THE SUPPORT VECTOR MACHINE METHOD. ["eprint_fieldopt_thesis_type_tugasakhir" not defined] thesis, Informatics.

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Abstract

ABSTRACT The Covid-19 pandemic has had a significant impact on various aspects of life. The government issued a vaccination policy as a form of handling the spread of the Covid-19 virus. This policy has created a pros and cons polemic in society, this is caused by confusion in information about the safety and benefits of vaccines. This research aims to design and implement a Covid-19 vaccination analysis system to determine the public's views on the Covid-19 vaccination policy. The data used in this research are tweets from Indonesian language Twitter users related to Covid-19 vaccination. Tweet data will go through a preprocessing process to clean the text from special characters and unimportant words. After cleaning, the data will go through a labeling stage using a lexicon and manual correction to reduce the risk of losing context. Next, the TF-IDF algorithm will be used to assign weights to words that reflect the importance of the words in the document. The sentiment analysis system uses the Support Vector Machine method for the model formation process. The result of this research is a web-based sentiment analysis system that can classify tweet data into positive and negative classes with an accuracy rate of 86.05%. Keywords: Vaccination, Sentiment Analysis, Support Vector Machine

Item Type:	Thesis (["eprint_fieldopt_thesis_type_tugasakhir" not defined])
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Sains Dan Teknologi > S1 Informatika
Depositing User:	Kaprodi S1 Informatika UTY
Date Deposited:	17 May 2024 01:50
Last Modified:	17 May 2024 01:50
URI:	http://eprints.uty.ac.id/id/eprint/15471

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