Studi Komparatif Algoritma KNN, SVM, dan Naive Bayes untuk Analisis Sentimen pada Review Google Play Aplikasi Traveloka - Nurul Siti Mukaromah; Nur Wakhidah (Universitas Boyolali, 2025)
Sentiment analysis has become an important approach in understanding user opinions towards digital applications, especially on review platforms such as the Google Play Store. This study aims to compare the performance of three popular classification algorithms, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes, in classifying sentiment in Traveloka application reviews. The methods used include collecting user review data from Google Play, preprocessing stages such as tokenization and stopword removal, and applying the three algorithms to the cleaned data. The evaluation was carried out using accuracy, precision, recall, and f1-score metrics. The dataset used consisted of 5000 review data that were evenly divided between positive and negative sentiments. The results showed that the SVM algorithm provided the best performance with an accuracy of 88%, followed by Naive Bayes at 86%, and KNN at 87%. The conclusion of this study states that SVM is more reliable in handling text-based sentiment analysis in the context of mobile application reviews. These findings are expected to be a reference in developing a more accurate sentiment analysis system for business needs and future research.