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All Journal Teknika Jurnal Sains dan Teknologi Jurnal Simetris JSI: Jurnal Sistem Informasi (E-Journal) International Journal of Advances in Intelligent Informatics IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang WIDYA LAKSANA Jurnal Informatika Kaputama (JIK) EVOLUSI : Jurnal Sains dan Manajemen JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Ilmu Teknik dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Infotek : Jurnal Informatika dan Teknologi JUSTIAN - Jurnal Sistem Informasi Akuntansi J-Intech (Journal of Information and Technology) Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Abdimas Le Mujtamak TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Dedikasi Saintek Jurnal Pengabdian Masyarakat SOROT: Jurnal Pengabdian Kepada Masyarakat Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat Indonesian Community Service Journal of Computer Science
Riski Annisa
Universitas Bina Sarana Informatika
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Journal : TAMIKA: Jurnal Tugas Akhir Manajemen Informatika

 1 
Perbandingan Kinerja Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam Analisis Sentimen Mobile Legends Zikirlah, Hikmawan Alvin; Iltavera Paula; Muhammad Fazilla; Riski Annisa; Lady Agustin Fitriana
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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Abstract

The rapid advancement of information and communication technology has significantly increased the popularity of online games in Indonesia, one of which is Mobile Legends: Bang Bang (MLBB), with millions of active users. The abundance of user reviews on digital platforms provides valuable data for analysis using text mining and natural language processing (NLP) approaches. Sentiment analysis is applied to classify user opinions into positive, negative, and neutral categories, offering insights into player satisfaction and perceptions of game quality. This study compares the performance of three classification algorithms, Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), in analyzing sentiment from Mobile Legends user reviews on the Google Play Store. A total of 5,000 reviews were collected using the web scraping technique and processed through the Knowledge Discovery in Databases (KDD) framework, which includes cleaning, case folding, tokenization, normalization, and stopword removal. Sentiment labeling was performed using a lexicon-based approach with the InSet sentiment lexicon. The dataset was divided into training and testing sets with an 80:20 ratio and evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm achieved the highest accuracy of 88.1%, followed by KNN at 65.1% and NB at 62.6%. Thus, SVM is recommended as the most effective model for sentiment analysis of Mobile Legends user reviews.
Co-Authors Agung Sasongko Anna Anna Anna Anna Anna Anna, Anna Anna, Anna - Arvin Hidayat Waluya Baihaqi, Muhammad Rivaldi Chika Suhendra Detylia Detylia Dewi Ayu Nur Wulandari Dharmawan, Weiskhy Steven Didi Rosiyadi Dwiza Riana Eliska, Puspa Eva Meilinda Fadilah, Aldi Firly Annisa Luthfi Hidayana, Reymond Syahputra Iltavera Paula Lady Agustin Fitriana Lestari Yusuf Marelita Marelita Maulana, Reza - Mohammad Kamal Reza Monikka Nur Winnarto Muhammad Fazilla Murdiansyah Murdiansyah Nur W, Monikka Nurfia Oktaviani Syamsiah Panny Agustia Rahayuningsih Parwandar, Robi Rahayuningsih, Agustia Raja Sabaruddin Raja Sabaruddin Raja Sabarudin Ramadhani, Zulfikar Ismaya Robi Parwandar Sendi Yustisio Siti Nurdiani Sri Murni Suhendra, Chika Wahyu Nugraha Welly, Welly Winnaarto, Monikka Nur Winnarto, Monikka Nur Yulia Yulia Yustisio, Sendi Zikirlah, Hikmawan Alvin