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September 10, 2025Jurnal Rimba Riset Ilmu manajemen Bisnis dan Akuntansi

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

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Authors

DVDwi Andre VebriansyahNYNiluh Komang Kusuma YasariDSDaris Itsar Samudra

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Overview

Analysis shows 40.7% positive and 49.3% negative sentiments in customer reviews, highlighting issues in customer service.

Key Points

  • Analysis reveals 40.7% of user reviews express positive sentiment, while 49.3% show negative sentiment.
  • Latent Dirichlet Allocation algorithm extracted seven topics with a coherence score of 0.508343 from negative reviews.
  • Service quality analysis indicates that reliability is the main concern, highlighting system instability and transaction failures.
  • Recommendations focus on enhancing system reliability and responsiveness to improve overall customer satisfaction.

Cite This Study

Vebriansyah et al. (2025) studied this question.

synapsesocial.com/papers/68c1c22d54b1d3bfb60ef53chttps://doi.org/10.61132/rimba.v3i2.1751
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Sentiment Classification of User Reviews for KAI Access Application Using Naive Bayes Method2024
  2. 2Development of a Software Usage Loyalty Model by Analysing the Causal Relationships between Software Quality, Customer Satisfaction, Trust, and Experience in the Access by KAI Application2025
  3. 3Sentiment Analysis of Transjakarta App Reviews Using the Naive Bayes Algorithm2025
  4. 4Identify Issues That Influence Customer Satisfaction Using Sentiment Analysis and Topic Modeling2024
  5. 5Sentiment Analysis of Jakarta Kini (JAKI) Application Reviews using the Naive Bayes Method2025