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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Jaya, April Kurniawan
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Sentiment Analysis of Mobile Legends Game using Naïve Bayes, K-Nearest Neighbors and Support Vector Machine Algorithm Sanjaya, Samuel Surya; Jaya, April Kurniawan; Candra, Rikky; Zang, Stefven
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1840

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.
Co-Authors Candra, Rikky Sanjaya, Samuel Surya Zang, Stefven