Analisis Sentimen Terhadap Aplikasi Canva Menggunakan Algoritma Naive Bayes Dan K-Nearest Neighbors

Abstract

The sentiment analysis used in the Canva application involves collecting user reviews or feedback. Then, a sentiment analysis algorithm is applied to classify the reviews as positive or negative. Sentiment analysis can help the company understand user opinions about the Canva application and how the application can meet user needs. The process of sentiment analysis in the Canva application involves collecting user reviews or feedback, which are then classified using a sentiment analysis algorithm. The research results show that the KNN algorithm has an accuracy rate of 83.92%, while Naive Bayes only has an accuracy rate of 77.41%. The KNN algorithm also has higher recall and precision values than Naive Bayes, namely 83.66% and 84.49%, respectively. In addition, the AUC value generated by the KNN algorithm is also higher than Naive Bayes, namely 95.00% compared to 94.50%. Therefore, it can be concluded that the KNN algorithm is more suitable for data classification in this research. This research can contribute to the development of the Canva application and improve the quality of service for its users.