COMPARISON OF ACCURACY LEVELS OF SVM, DECISION TREE AND RANDOM FOREST ALGORITHMS IN SENTIMENT ANALYSIS OF USER RESPONSES OF THE GOPAY APPLICATION - Indriani, Indriani; Ade Davy Wiranata (Jurnal Teknik Informatika (Jutif), 2024)
The development of technology from time to time makes all work or activities easier, one of which is online money transactions which are called e-wallets or digital wallets. One of the digital wallet applications that is often used is GoPay, which is a platform and tool created for making digital payments. Not long ago, GoPay was separated into one application, which previously existed in the Gojek application. However, every application certainly has a negative side, such as GoPay, where to use the application you have to be connected to the internet, which creates dependence on smartphones. Based on this problem, the company needs to know the response of users of the GoPay application which has been launched using the SVM, Decision Tree and Random algorithms. Forest. Therefore, the aim of this research is to carry out sentiment analysis on the responses of GoPay application users after being separated from Gojek and to find out the comparison of evaluation results or accuracy produced by the three algorithms. The results of this research show that of the three algorithms used, Positive sentiment is more than Negative sentiment, where in SVM Positive 89% and Negative 85%, Decision Tree class Positive 89% and Negative 76% while in Random Forest class positive 93% and Negative 86 %. Apart from that, the Random Forest algorithm has a high level of accuracy, namely 90%, then the SVM algorithm 88% and the Decision Tree algorithm 84%.