Penerapan Ensemble Learning Menggunakan Metode Support Vector Machine, Naïve Bayes Classifier, dan Valence Aware Dictionary for Sentiment Reasoning untuk Meningkatkan Akurasi Sentiment Analysis pada Review Aplikasi Google Play

Tania Sunyoto (Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya)
Djoni Haryadi Setiabudi (Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya)
Alvin Nathaniel Tjondrowiguno (Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya)

Jurnal Infra
Vol 10, No 2 (2022)

In an age where almost everyone owns a smartphone, more and more mobile applications are being developed and distributed to Google Play. To decide which application to download, customers are influenced by ratings and reviews. Reviews provide more information than ratings, but there are so many that they are difficult and take a long time to obtain. The application of sentiment analysis supported by high accuracy in reviews can make it easier for customers to get sentiment information from th e application and help them make decisions to download / use the application or not. This research uses a combination of Naïve Bayes and SVM machine learning models with the VADER lexicon model, then Ensemble Learning is carried out using Majority Voting, Majority Weighted Voting, and Stacking to improve accuracy. The results of this system indicate that by using Ensemble Learning the accuracy result increases but not significantly even decreases from SVM results of 88.88% to 88.87% using Stacking.