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All Journal Journal of Intelligent Systems Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika Budimas : Jurnal Pengabdian Masyarakat Jurnal Infortech International Journal Of Science, Technology & Management (IJSTM) Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Algoritme Jurnal Mahasiswa Teknik Informatika INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Pengabdian Kreatif Cemerlang Indonesia AMMA : Jurnal Pengabdian Masyarakat Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Prawara Jurnal Pengabdian Masyarakat
Tyas Setiyorini
Universitas Nusa Mandiri
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Journal : Algoritme Jurnal Mahasiswa Teknik Informatika

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Analisis Sentimen Ulasan Mobile Legend Menggunakan Algoritma Naive Bayes, SVM, Logistic Regression Alengka, Son Gohan; Putra, Jordy Lasmana; Setiyorini, Tyas
Jurnal Algoritme Vol 5 No 3 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i3.12915

Abstract

The rapid growth of the mobile gaming industry in Indonesia, particularly Mobile Legends: Bang-Bang, has generated millions of user reviews on the Google Play Store, making manual analysis inefficient and prone to bias. This study compares three algorithms—Naive Bayes, Support Vector Machine (SVM), and Logistic Regression—for sentiment analysis of 52,651 reviews. Preprocessing includes text cleaning, stopword removal (Indonesian/English), Sastrawi stemming, and TF-IDF representation (min_df=3, max_df=0.9, n-gram 1–2). Binary labeling follows a rating-based approach: 1–2 stars (negative), 4–5 stars (positive), while 3-star reviews are excluded due to ambiguity. Evaluation using accuracy, precision, recall, F1-score, confusion matrix, and Cohen’s Kappa shows SVM and Logistic Regression achieving ≈90–91%, with SVM chosen as the default model for its balanced metrics and margin stability. The model can be deployed as an API service (Flask/FastAPI) for near real-time review monitoring (e.g., lag, AFK, matchmaking), enabling alert thresholds and improvement prioritization. Findings remain limited to Mobile Legends reviews on Google Play, requiring further validation across other applications.
Co-Authors Achmad Rifai ACHMAD RIFAI Aditiya Yoga Pratama Albert Riyandi Alengka, Son Gohan Angga Subrata Anggie Andriansyah Dedi Saputra Fitra Septia Nugraha Frieyadie Frieyadie Frieyadie Harsih Rianto Ibnu Alfarobi Ibnu Rusdi Istiyanto, Ilham Jordy Lasmana Putra Kudiantoro Widianto Kudiantoro Widianto, Kudiantoro Maryanah Safitri Maryanah Safitri Muhamad Ryansyah Nofarenzi, Yosari Nur Mutia Eka Pusparani Nurajijah Rismaniah Rismaniah Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Romi Satria Wahono Sita Anggraeni Sita Anggraeni Sita Anggraeni, Sita Siti Nurdiani Solehudin, Rizki Sri Syaifur Rahmatullah Syaifur Rahmatullah, Syaifur