Sentiment Analysis of KAI Access App Customer Reviews to Improve Customer Service Using Natural Language Processing

Dwi Andre Vebriansyah; Niluh Komang Kusuma Yasari; Daris Itsar Samudra; Titis Shinta Dhewi

doi:10.61132/rimba.v3i2.1751

Authors

Dwi Andre Vebriansyah Universitas Negeri Malang
Niluh Komang Kusuma Yasari Universitas Negeri Malang
Daris Itsar Samudra Universitas Negeri Malang
Titis Shinta Dhewi Universitas Negeri Malang

DOI:

https://doi.org/10.61132/rimba.v3i2.1751

Keywords:

Customer Relationship Management, KAI Access, Latent Dirichlet Allocation, Natural Language Processing, Sentiment Analysis, Service Quality

Abstract

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.

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