Perbandingan Naive Bayes dan K-Nearest Neighbor untuk Analisis Sentimen Aplikasi Gapura UB Berdasarkan Ulasan Pengguna pada Playstore - Robiata Tsania Salsabila Aditya Putri; Dian Eka Ratnawati; Dwija Wisnu Brata (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2023)
The Gapura UB application is a mobile apps-based application that makes it easy for students to access services and information related to Universitas Brawijaya such as online presence, viewing class schedules, and accessing KRS and KHS. Behind the convenience offered by the application, it was found that the performance of the application was not optimal, such as the emergence of bugs or errors in the application and the discovery of reviews on the Google Playstore that indicated user dissatisfaction with the performance of the application by containing complaints regarding the application. This research tries to classify Gapura UB application user reviews through Google Playstore by comparing two classification algorithms, namely Naive Bayes and K-Nearest Neighbor which aims to find out which algorithm is superior in Gapura UB application user review data. The review data used is 300 data divided into two sentiment classes, namely positive sentiment and negative sentiment. The results showed that the Naive Bayes algorithm has superior performance than the K-Nearest Neighbor with 88.5% accuracy, 88.7% precision, 88.2% recall, and 88.2% f-measure while the K-Nearest Neighbor produces 84.8% accuracy, 85.4% precision, recall 84.6% and 84.1% f-measure obtained from the value of k = 5.