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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Jurnal Informatika Kaputama (JIK) Syntax: Journal of Software Engineering, Computer Science and Information Technology Prosiding Snastikom Jurnal Nasional Teknologi Komputer International Journal of Health, Engineering and Technology Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology Repeater: Publikasi Teknik Informatika dan Jaringan
Imran Lubis
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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A RECURRENT NEURAL NETWORK–BASED SENTIMENT ANALYSIS OF MOBILE LEGENDS APP REVIEWS Naufal Ilmi Rangkuti; Imran Lubis
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 1 (2026): January 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i1.390

Abstract

With the rapid growth of mobile applications, user reviews have become a valuable source of feedback for developers. This study investigates the use of a Recurrent Neural Network (RNN) for sentiment analysis of Mobile Legends user reviews. The textual data were preprocessed through cleaning, tokenization, and padding, while sentiment scores were converted into categorical labels. A Sequential RNN model, consisting of an Embedding layer, a SimpleRNN layer, and a Dense output layer with softmax activation, was constructed to classify reviews into three sentiment categories: negative, neutral, and positive. During training, the model achieved approximately 75% accuracy, and the Mean Squared Error (MSE) was 0.1354. However, evaluation using the classification report and confusion matrix revealed that the model was biased toward the negative class due to class imbalance, failing to correctly classify neutral and positive reviews. The high overall accuracy was misleading, as the model’s performance was limited by the dominance of the negative class. These results highlight the limitations of using a simple RNN architecture for multi-class sentiment classification in imbalanced datasets. To improve performance, future work should consider balancing the dataset through resampling or synthetic data generation and employing more advanced sequential models, such as LSTM or GRU, possibly combined with attention mechanisms or pretrained language models, to better capture the characteristics of all sentiment classes.
Co-Authors Abdul Jabbar Lubis Andi Marwan Elhanafi Daffa Raihan Fikri Akbar Syah Putra Nasution Irfan Jaya Kristina Anastasia Br. Sitepu Leriansyah Putra Nasyuli Marto Sihombing Mira Asmara Zega Muhamad Fadil Muhammad Fauzy Naufal Ilmi Rangkuti Rahmad Anugerah Sy Lubis Sahfira Isnaini Lubis Tommy Ummul Khair