TOPIC MODELING ANALYSIS OF ACCESS BY KAI APPLICATION REVIEWS ON GOOGLE PLAY STORE USING LATENT DIRICHLET ALLOCATION

Abstract

PT Kereta Api Indonesia (KAI) has released a ticket booking application named Access by KAI, which has been downloaded over 10 million times and has received more than 187,000 reviews on Google Playstore. However, with the vast amount of review data for the Access by KAI application, it is still challenging to understand the aspects that need improvement. In this case, topic modeling is necessary to classify the reviews. The aim of this research is to apply the Latent Dirichlet Allocation (LDA) method to model topics of user reviews of the Access by KAI application on Google Playstore and to present recommendations derived from the data dictionary or bag-of-words through a fishbone diagram. This research uses the lifecycle of the data mining methodology, which consists of the stages of problem definition, selecting text data mining approach, data collecting, text standardization, text processing, feature extraction, analysis, and discovery. The results of this research identified a total of 7 topics with a coherence score of 0.40279302. The conclusions from each topic are as follows: Topic 1 discusses application updates, available versions, interface, and the relationship with stations and cities. Topic 2 involves users complaining about decreased application performance after updates. Topic 3 covers the use of the Access by KAI application to book train tickets, highlighting the app version, user experience, and app quality ranging from good to cumbersome. Topic 4 reports user difficulties in accessing, particularly issues with login and payment after app updates. Topic 5 focuses on login difficulties, slow app performance, and issues in the ticket booking and payment process. Topic 6 reflects user disappointment regarding performance decline in speed and login difficulties after updates. Topic 7 addresses user complaints about difficulties in purchasing train tickets through the KAI app following updates or upgrades.