ANALISIS SENTIMEN PADA REVIEW RESTORAN DENGAN TEKS BAHASA INDONESIA MENGUNAKAN ALGORITMA NAIVE BAYES - Dinda Ayu Muthia (JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), 2017)
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 is 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 a 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, 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%.