PENERAPAN METODE NAÏVE BAYES CLASSIFIER PADA SENTIMEN ANALISIS APLIKASI INVESTASI KEUANGAN DIGITAL

Studi Kasus: Bareksa Dan Bibit

Abstract

Investing online is a very promising opportunity. There are many online investment enthusiasts who do not understand how to invest online correctly and be able to minimize risk. Lack of public understanding of the investment implementation process can lead to fraud by irresponsible parties. So understanding investing online is very necessary. There are many online investment applications on the Google Play Store, but these investment applications have their own advantages and disadvantages. The objects of research are the applications of Bareksa and Seeds because the news media often report on these applications at the top and selecting an application requires a collection of information obtained from previous user reviews. The method used is the Naïve Bayes Classifier. Based on the results, the classification is divided into 3 (three) sentiments, namely positive, negative and neutral. With a comparison of training data and testing data 70%:30% accuracy in the Bareksa application was obtained 54% and 44% in the seed application.