Analisis Sentimen Data Ulasan Pengguna Aplikasi TIX ID di Indonesia pada Google Play Store menggunakan Support Vector Machine - Muhammad Razan Nadhif; Dwija Wisnu Brata; Bayu Rahayudi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2022)
Review on the Google Play Store is one of the features used to provide an assessment of an application. TIX ID is an online cinema ticket booking service or application company made by PT Nusantara Elang Sejahtera. This application provides services such as ordering movie tickets from your favorite cinema, choosing online movies to buy or rent, and buying vouchers to watch streaming movies online. However, the rating accompanied by various reviews does not mean that the wishes or problems of users are fulfilled and stop evaluating in improving services to users. For this reason, sentiment analysis is needed that can classify reviews as user sentiment. In this study, the scraping stage was carried out for collecting application user review data, followed by the text preprocessing stage to process data by selecting data and turning it into more structured data. The data from text preprocessing were word weighted using the Term Frequency - Inverse Document Frequency (TF-IDF) method. Then sentiment classification is carried out using the Support Vector Machine (SVM) algorithm. The best results obtained with the SVM algorithm for sentiment testing of 2 classes with unbalanced data with positive data 274 and negative data 100 using training data values and test data 90%:10%, total data with 374 data, using parameter value C = 10, cross validation experiment K=10 and use of linear kernel. The results obtained for the average value of 91% accuracy, 94% precision, 83% recall and 86% f-measure. The best results obtained with the SVM algorithm for testing sentiment of 2 classes with balanced data with positive data 150 and negative data 150 using training data values and test data 90%:10%, total data with 300 data, using parameter value C = 0.5, experiment cross validation K=10 and use of linear kernel. The test results on balanced data get the highest accuracy results, namely 94%, precision 94%, recall 94% and f-measure 94% in the Support Vector Machine method with 2 sentiment classification.