Sentiment Analysis Towards the KitaLulus Application Using the Naive Bayes Method from Google Play Store Reviews

Nadia Amalia Putri (Universitas Duta Bangsa, Indonesia)
Agustina Srirahayu (Universitas Duta Bangsa, Indonesia)
Nugroho Arif Sudibyo (Universitas Duta Bangsa, Indonesia)

Jurnal Indonesia Sosial Teknologi
Vol. 5 No. 10 (2024): Jurnal Indonesia Sosial Teknologi

Job search apps like KitaLulus are essential in helping graduates find jobs based on their skills and interests. Sentiment analysis is needed to understand user opinions about the KitaLulus application. The Naive Bayes method is used in this analysis because of its high efficiency and accuracy. This research used 597 data and achieved an accuracy rate of 91%. The eval_uation results show positive sentiment values for precision, recall, and f1-score of 0.99, 0.94, and 0.97 respectively. On the other hand, the model performance is low for negative and neutral sentiments. The aim of this research is to increase user understanding of the KitaLulus application and provide valuable assistance to developers in their efforts to improve the quality of the application.