Analisis Data Sentimen Ulasan Aplikasi Dana di Google Play Store Menggunakan Algoritma Naïve Bayes - Windy Astuti; Rudi Kurniawan; Yudhistira Arie Wijaya (Jurnal Informatika dan Rekayasa Perangkat Lunak, 2024)
In an era of rapid technological advancement, mobile applications, especially e-wallets such as Dana Apps, are becoming increasingly easy to use for digital payment transactions. This innovation drives research on how digital payment platforms build information systems and business strategies through digital platforms. To compete in a growing industry, companies must produce products and services that meet customer needs. Internet user opinions, especially about apps, are important in gathering information. This research focuses on analyzing the sentiment data of Dana App reviews on the Google Play Store using the Naïve Bayes classification algorithm which is well known for its accuracy and high data processing speed. The results show that the 80:20 ratio provides an accuracy rate of 50.21%, precision and recall of 0.00% each, This research has a significant impact on the information technology industry, providing guidance for practitioners and researchers in choosing sentiment data analysis methods. The implementation of the Naïve Bayes algorithm can improve the interaction between users and applications, support innovation, and contribute to the overall development of information technology.