SELEKSI FITUR INFORMATION GAIN PADA ANALISISSENTIMEN TERHADAP ULASAN APLIKASI FLIP DENGANALGORITMA SUPPORT VECTOR MACHINE - Isma'il Muhammad Falih (2022)
Flip is an application to make transfers between different banks at no cost and provides services for buying credit and data packages that generally work as a bridge for transactions between different banks. Reviews given from Flip application users contain many constructive and critical opinions that can be used as input for Flip application developers. The purpose of this study is to build a sentiment classification model using the Support Vector Machine method and the Information Gain feature selection method for the Flip application review on Google Play services. In this study, the reviews will be divided into two categories that is positive and negative based on manual labeling by 3 assessors, which is then carried out preprocessing, feature selection, and split data of 80% data train and 20% data test before modeling. There are two models, namely a model without feature selection (SVM model) and a model with feature selection (SVM-IG model). The evaluation results show an accuracy is 91.97%, precision is 95.53%, recall is 91.45%, and AUC is 0.9215 for the SVM model, while for the SVM-IG model the accuracy is 96.25%, precision is 99.10%, recall is 94.87%, and AUC is 0.9672.