ANALISIS SENTIMEN PADA REVIEW RESTORAN DENGAN TEKS BAHASA INDONESIA MENGUNAKAN ALGORITMA NAIVE BAYES
Abstract: In the era of the
web as it is now, some information is now flowing through the network. Because
of the variety of web content includes subjective opinion and objective
information, it is now common for people to gather information about products
and services they want to buy. However, because there are quite a lot of
information in text form without any numerical scale, it is difficult to
classify the evaluation of information efficiently without reading the complete
text. Sentiment analysis aims to address this problem by automatically
classifying user review be positive or negative opinion. Naïve Bayes classifier
is a popular machine learning techniques for text classification, because it is
very simple, efficient and performs well in many domains. However, Naïve Bayes
has the disadvantage that is very sensitive to feature too much, resulting in a
classification accuracy becomes low. Therefore, in this study used the method
of selecting features, namely Genetic algorithm in order to improve the
accuracy of Naïve Bayes classifier. This research resulted in the
classification of the text in the form of a positive or negative review of the
restaurant. Measurement is based on the accuracy of Naive Bayes before and
after the addition of feature selection methods. The evaluation was done using
a 10 fold cross validation. While the measurement accuracy is measured by
confusion matrix and ROC curves. The results showed an increase in the accuracy
of Naïve Bayes from 86.50% to 90.50%.
Penulis: Dinda Ayu Muthia
Kode Jurnal: jptkomputerdd170252
