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All Journal Idealis : Indonesia Journal Information System
Ahmad Alif Candra Selamet
Sistem Informasi, Fakultas Teknik, Universitas Muria Kudus, Kudus, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management

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Analisis Sentimen Ulasan Mobile Legends: Bang Bang dalam Bahasa Indonesia Menggunakan Random Forest, KNN, TF-IDF, dan SMOTE Ahmad Alif Candra Selamet; Pratomo Setiaji; Wiwit Agus Triyanto
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3819

Abstract

Mobile Legends: Bang Bang (MLBB) is one of the most popular mobile games, generating a large number of user reviews on the Google Play Store. The large volume of reviews makes manual sentiment analysis impractical, requiring an automated machine learning approach. This study compares the performance of Random Forest and K-Nearest Neighbors (KNN) for classifying sentiment in Indonesian-language MLBB reviews. A total of 6,994 reviews were obtained through web scraping and preprocessing. The proposed framework includes text preprocessing, rating-based sentiment labeling, TF-IDF feature extraction, SMOTE-based class balancing, model training, and evaluation using Accuracy, Precision, Recall, F1-score, Macro-F1, Weighted-F1, and 5-fold cross-validation. Random Forest with SMOTE achieved the best performance, with an Accuracy of 84.0% and a Macro-F1 score of 80.8%, outperforming KNN with SMOTE, which achieved 73.8% Accuracy and 71.9% Macro-F1. The ablation study demonstrates that the effectiveness of SMOTE is model-dependent, improving Random Forest but degrading KNN performance. This study provides empirical evidence of SMOTE impact on different classifiers and employs cross-validation-based K selection to prevent test data leakage.
Co-Authors Pratomo Setiaji Wiwit Agus Triyanto