Implementasi Particle Swarm Optimization (PSO) pada Analysis Sentiment Review Aplikasi Trafi menggunakan Algoritma Naive Bayes (NB)
Abstract - The development of
transportation applications is now getting bigger so that many vendors
compete for business in creating transportation mode applications,
starting from the quality and quantity so that it is often questioned.
With this, the researcher held a transportation application called Trafi
to get opinions or comments on applications from people who had used
the application and poured it into online media. Of the many comments
reviewed to obtain a set of positive and negative forms of data from the
text that the researcher will process. For classification data using
Naïve Bayes (NB), NB is one of the most popular algorithms for pattern
recognition. Apart from simplicity, the Naive Bayes classifier is a
popular machine learning technique for text classification, Particle
Swarm Optimization (PSO) which combines with the Naive Bayes
classification to improve performance. Before use, optimization with PSO
in the data set accuracy obtained was 69.50% and after the combination
of Naive Bayes and PSO accuracy was 72.34%. Use PSO and Naïve Bayes
according to the concept of text mining which aims to find patterns that
exist in text, the activity carried out by text mining here is text
classification.
Keywords
Sentiment Analysis, Android Appstore Product Review, Naive Bayes Algorithm
Keterangan: Jurnal Teknik Komputer 7 (1), 25-29/7/1/LPPM Universitas Bina Sarana Informatika
Link Jurnal : https://ejournal.bsi.ac.id/ejurnal/index.php/jtk/article/view/9078
