Sentiment Analysis of Online Investment Applications on Google Play Store using Random Forest Algorithm Method
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Abstract
Online investment is an investment to get a long profit. Where this investment has a function to store funds long-term and in order to have a higher value than annual inflation. Currently online and offline investments are available in the market to reach the increasing interest of beginner investors. In addition, to open an online investment account can also now be very easy, fast and flexible. There are many online investment apps that can be downloaded through the Google Play Store. However, with the number of stock investment applications sometimes makes one have to choose which application is the best that can be used. Therefore, the author conducted a study to find out the best stock investment application to be recommended to investors who will start investing. The purpose of this study is to analyze the sentiment of online investment application reviews, namely stockbit, Hsb Investment, and Seedlings. In this study, the method to be used is the Random Forest method. Based on the analysis and testing conducted, the conclusion that can be drawn is the result of the implementation of random forest algorithms for Stockbit applications in this study resulted in an accuracy value of 62.50%, for seed applications in this study resulted in an accuracy value of 63.39%. And for HSB applications in this study resulted in an accuracy value of 96.25%.
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