ANALISIS SENTIMEN ARTIKEL BERITA TOKOH SEPAK BOLA DUNIA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES BERBASIS PARTICLE SWARM OPTIMIZATION

research
  • 29 Apr
  • 2019

ANALISIS SENTIMEN ARTIKEL BERITA TOKOH SEPAK BOLA DUNIA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES BERBASIS PARTICLE SWARM OPTIMIZATION

Information about the actual news that occurs every day, or what happens every minute that can
now be easily obtained such as general online news sites containing various actual information,
as well as news sites that have special rubrics, for example news about politics, economics,
education , entertainment, sports and so on. by using sentiment analysis by classifying
documents with text mining. The algorithm used in this study is Naive Bayes and Support Vector
Machine based on Particle Swarm Optimization. The results obtained from testing NB, NB
(PSO), SVM and SVM (PSO) data will be compared. SVM (PSO) accuracy has a higher
accuracy compared to SVM, NB and NB (PSO). So it can be concluded that the best optimization
application in this model is that Support Vector Machine based on Particle Swarm Optimization
(PSO) can provide a solution to classification problems in the case of sentiment analysis of
world football figures Lionel Messi.

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    Analisis Sentimen Artikel Berita Tokoh Sepak Bola Dunia Menggunakan Algoritma Support Vector Machine Dan Naive Bayes Berbasis Particle Swarm Optimization

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