ANALISIS ULASAN APLIKASI TINDER MENGGUNAKAN ALGORITMA NAÏVE BAYES DENGAN OPTIMASI INFORMATION GAIN
Abstract
Current technological developments are very rapid, including the scope of developments in information and communication technology. In this digital era, there are more and more types of applications, the existence of this online dating application makes it efficient and effective for people to interact by utilizing the application features available in the application, one of the applications is Tinder. The Tinder application is one of the most downloaded online dating applications, namely 67 million downloads. Users who download the Tinder application can provide reviews or comments on the application with the features provided on Google Play. Seeing the importance of reviews on an application, it is necessary to carry out sentiment analysis which aims to find out how Indonesian users respond to the online dating application Tinder through reviews on Google Play. The algorithm used to classify sentiment is the Naïve Bayes algorithm with the addition of feature selection with Information Gain. The research methodology used is Knowledge Discovery in Database. The results of research using the Naïve Bayes method get an accuracy of 68%, while using the Naïve Bayes method using the Information Gain feature selection gets an accuracy of 78%. Using the Naïve Bayes method using Information Gain feature selection produces higher accuracy results than without using Information Gain feature selection.
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