Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Debi Handika
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Topic Modeling of Clash of Clans Player Reviews Using NLP-Based Latent Dirichlet Allocation (LDA) Machine Learning Method Rai Markus Panamuan; Debi Handika; Muhamad Rizki Pratama; Weiskhy Steven Dharmawan; Lady Agustin Fitriana; Riski Annisa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2364

Abstract

The rapid growth of the mobile gaming industry has generated millions of player reviews on platforms like the Google Play Store. Clash of Clans, developed by Supercell, is one of the world's most popular mobile strategy games, generating a vast volume of user reviews that are difficult to analyze manually. This study applies Latent Dirichlet Allocation (LDA), a generative probabilistic machine learning model based on Natural Language Processing (NLP), to identify and cluster key topics discussed in player reviews on the Google Play Store. A total of 10,000 player reviews were collected through web scraping, followed by NLP-based text preprocessing including tokenization, stopword removal, and lemmatization. The LDA model was optimized using a coherence score evaluation of 0.512, resulting in the identification of five dominant discussion topics: technical issues and bugs, game updates and balance, gameplay and strategy, monetization and in-app purchases, and social interactions and clan systems. The results show that LDA-based topic modeling provides structured and actionable insights for game developers to understand player feedback and improve game quality. This research contributes to the field of NLP-based mobile game review analysis.
Co-Authors Lady Agustin Fitriana Muhamad Rizki Pratama Rai Markus Panamuan Riski Annisa Weiskhy Steven Dharmawan