Sentiment Analysis of Mobile Legends Game using Naïve Bayes, K-Nearest Neighbors and Support Vector Machine Algorithm

Samuel Surya Sanjaya; April Kurniawan Jaya; Rikky Candra; Stefven Zang

doi:10.59934/jaiea.v5i2.1840

Authors

Samuel Surya Sanjaya Institut Bisnis dan Teknologi Pelita Indonesia
April Kurniawan Jaya Institut Bisnis dan Teknologi Pelita Indonesia
Rikky Candra Institut Bisnis dan Teknologi Pelita Indonesia
Stefven Zang Institut Bisnis dan Teknologi Pelita Indonesia

DOI:

https://doi.org/10.59934/jaiea.v5i2.1840

Keywords:

Analisis Sentimen; K-Nearest Neighbors; Klasifikasi Teks; Mobile Legends; Naïve Bayes; Support Vector Machine

Abstract

Sentiment analysis of Mobile Legends: Bang Bang (MLBB) user reviews is very important for understanding public satisfaction and perspectives. Therefore, this study aims to analyze and compare the performance of three Machine Learning algorithms: Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) in classifying user review sentiments. A supervised machine learning approach was applied using 6,000 reviews obtained from a secondary Kaggle dataset, involving Data Preprocessing and Feature Extraction (TF-IDF) stages, followed by an 80:20 Data Split for model training. The comparison of metric results shows that the Support Vector Machine (SVM) model provides the best overall performance, achieving 79.88% Accuracy and 78.06% F1-Score, although NB slightly outperforms in the Precision metric. In conclusion, SVM's performance proves this algorithm is superior in classifying Indonesian-language mobile game review sentiments, providing strategic insights for MLBB developers in making service improvement decisions.

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