Perbandingan Naïve Bayes dan KNN dalam menganalisis sentimen pengguna terhadap UI/UX pada aplikasi IKD - Anumi, Maria Grassella; Manongga, Danny (Fakultas Teknologi Informasi Universitas Kristen Satya Wacana, 2025)
The Directorate General of Citizens and Civil Registration, under the Ministry of Home Affairs, has developed an application called digitization of population documents (IKD) to enhance administrative services related to community registration and increase user satisfaction. However, this application has its strengths and weaknesses, which have resulted in mixed reactions from users. To analyze user sentiment towards the application's user interface and user experience, the research utilized Naïve Bayes and KNN techniques. The study involved 52 respondents, and the results showed that the Naïve Bayes algorithm has a higher accuracy rate than the KNN algorithm. Sentiment predictions for the user interface obtained 38 positive responses, 12 neutral responses, and two negative responses, while for user experience, the study obtained 41 positive responses, eight neutral responses, and three negative responses. The accuracy rates of the Naïve Bayes algorithm for the user interface and user experience were 94.23% and 90.38%, respectively. On the other hand, the KNN algorithm achieves an accuracy rate of 71.15% for the user interface and 88.46% for the user experience. Overall, the study shows that the Naïve Bayes method outperforms the KNN method in terms of accuracy for user interface and user experience.