Penerapan Naïve Bayes Untuk Analisis Sentimen Pada Ulasan Aplikasi Mobile Legends
- Attila Elang Perkasa Universitas Semarang, Semarang, Indonesia
-
Astrid Novita Putri
*
Universitas Semarang, Semarang,
Indonesia
https://orcid.org/0000-0001-9075-3441
(*) Corresponding Author
Abstract
Indonesia has become one of the potential markets for the gaming industry, continually increasing the number of gamers. One of the most popular mobile games in Indonesia is Mobile Legends. Mobile Legends is an online multiplayer battle arena (MOBA) video game developed by Moonton, a game development company based in China. The influence of reviews on the reputation of an app also affects potential new users, whether the reviews are positive or negative. Research has shown that the Naïve Bayes model provides good accuracy for sentiment analysis. This study is expected to help understand the perceptions and experiences of players of the game. The study uses the KDD (Knowledge Discovery in Data) method due to its advantages in identifying organized patterns from a complex dataset, making the data easier to understand. During the research process, 320,513 positive reviews, 185,777 negative reviews, and 20,210 neutral reviews were obtained. The accuracy value remained constant at 87% for the 80:20 data split scenario. Performance on the negative class showed high precision at 88%. The negative recall was 92%, indicating that the model could accurately capture truly negative reviews. A stable F1-Score of 82% signifies a good balance between precision and recall for the negative class. Performance on the positive class showed 87% precision and 83% recall. The F1-Score between the two was nearly balanced, indicating that the model performed similarly for both labels overall.
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