This research conducted a systematic literature review of studies related to analyzing service quality based on user reviews with a machine learning approach. A total of 15 international and national journals were analyzed to identify challenges, methods, and trends in research in this aspect. The review results show that Natural Language Processing (NLP) and Sentiment Analysis techniques are the dominant approaches, with machine learning models such as Deep Learning, Naive Bayes, and Support Vector Machine (SVM) being commonly used. The review also identifies research gaps and provides recommendations for future research directions.

This research analyzes user sentiment reviews of the KAI Access application from Google Play Store to improve customer service at PT Kereta Api Indonesia. The study uses a Natural Language Processing (NLP) approach with the Latent Dirichlet Allocation (LDA) algorithm to extract main topics from 10,000 reviews collected from April 2024 to April 2025. Analysis results show 40.7% positive sentiment reviews and 49.3% negative. After data preprocessing through case folding, normalization, tokenization, stopword removal, and stemming, seven optimum topics were found from negative sentiment with a coherence score of 0.508343 and two optimum topics from positive sentiment with a coherence score of 0.511673. Analysis based on five service quality dimensions (tangibles, reliability, responsiveness, assurance, and empathy) reveals that the reliability dimension becomes the main issue, including system instability, transaction failures, login difficulties, and data inaccuracy. The responsiveness dimension is the second priority, with users expecting fast and responsive service to complaints. The results of this study provide recommendations for PT KAI to prioritize improvements in system reliability and responsiveness aspects to enhance the overall user experience, which will ultimately impact customer satisfaction and loyalty.