MARVYNA, HANIFAH CITRA
(2023)
ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP KENAIKAN HARGA BBM DI INDONESIA DENGAN ALGORITMA NAIVE BAYES.
[Undergraduate Thesis]
Abstract
The difficulty of manually classifying text data makes sentiment analysis a solution
to make it easier to classify data polarity. Twitter is one of the social media that
provides concise text data in the form of opinions on a topic. One of the topics that
is currently being discussed is the increase in the price of fuel oil (BBM) in
Indonesia. Through this research, an analysis of the sentiments of Twitter users
towards rising fuel prices in Indonesia is carried out using the Naive Bayes
algorithm. The method used in this study starts from data collection, text
preprocessing, data labeling, feature extraction, data s, classification and
evaluation of algorithm performance. The results of the analysis of Twitter users'
sentiment analysis on rising fuel prices in Indonesia with the Naïve Bayes
Algorithm are dominated by negative sentiments from 5,000 collected tweet data
divided into 54.6% negative sentiment, 31.8% positive sentiment, and 13.6%
neutral sentiment . While the best results of the classification performance of the
Naïve Bayes algorithm were obtained in the 80:20 data ratio experiment with an
accuracy value of 65%, a precision of 74%, a recall of 45%, and an error rate of
35%.
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