Aprinastya, Rachell
ANALISIS SENTIMEN GAME GENSHIN IMPACT MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES CLASSIFIER.
JUSIFO (Jurnal Sistem Informasi), 10 (2).
pp. 11-20.
ISSN ISSN: 2623-1662 (online) | 2460-092X (printed)
(Submitted)
Abstract
Genshin Impact is one of the popular games among gaming aficionados, thereby underscoring the criticality of conducting comprehensive user analysis research. The data used was collected from the App Store and Google Play Store, with the analysis considering both English and Indonesian to evaluate the effectiveness of each algorithm in different linguistic contexts. This study aims to compare the Support Vector Machine (SVM) algorithm with the Naive Bayes Classifier (NBC) algorithm to analyze user sentiment in reviews of the game Genshin Impact. The results of the study show different accuracy levels between the two algorithms, with the Naive Bayes Classifier (NBC) algorithm achieving a higher accuracy of 71%, followed by the Support Vector Machine (SVM) algorithm at 63%. Data using English achieved a higher average accuracy of 71%, compared to Indonesian, which had an average accuracy of 69%. This research contributes to the development of more accurate sentiment analysis methods and provides insights into language-specific challenges in machine learning.
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