ANALISIS SENTIMEN MENGGUNAKAN ALGORITMA NAÃVE BAYES
TENTANG REAKSI MASYARAKAT TWITTER TERHADAP
KENAIKAN BAHAN BAKAR MINYAK (BBM) TAHUN 2022
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Institusion
Universitas Muhammadiyah Sukabumi
Author
Supriatna, Wahyu Ramdan
Subject
T Technology (General)
Datestamp
2023-08-02 05:05:32
Abstract :
BBM is a type of fuel that is produced from the bowels of the earth processed in crude oil refineries. Fuel oil is very much needed by the Indonesian people with a large number of population mobility in Indonesia who have means of transportation. world oil caused the government to raise fuel prices, this had a negative impact on society. We can easily see the phenomenon of resistance to fuel price hikes through social media activities, one of which emerges from trending topics on Twitter with the hashtag #BBMnaik. Thus, the tweet data can be used to be processed so as to produce useful information. In the process of carrying out this research through several stages that must be carried out in order to show maximum results. The method used to find out the results of this study is the Naive Bayes method. The tweet data obtained in this study amounted to 1,351 data which will be classified into positive and negative classes. The experimental results used 6 sample documents, 5 as training data and 1 as test data. The probability value that appears in the experiment is 0.000225 which is in P(Positive | Negative) so that the sentiment classification is included in the "Positive" label.
BBM is a type of fuel that is produced from the bowels of the earth processed in crude oil refineries. Fuel oil is very much needed by the Indonesian people with a large number of population mobility in Indonesia who have means of transportation. world oil caused the government to raise fuel prices, this had a negative impact on society. We can easily see the phenomenon of resistance to fuel price hikes through social media activities, one of which emerges from trending topics on Twitter with the hashtag #BBMnaik. Thus, the tweet data can be used to be processed so as to produce useful information. In the process of carrying out this research through several stages that must be carried out in order to show maximum results. The method used to find out the results of this study is the Naive Bayes method. The tweet data obtained in this study amounted to 1,351 data which will be classified into positive and negative classes. The experimental results used 6 sample documents, 5 as training data and 1 as test data. The probability value that appears in the experiment is 0.000225 which is in P(Positive | Negative) so that the sentiment classification is included in the "Positive" label.
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