Abstract
Fintech OVO in Indonesia is an important part of cashless payment services. Users take advantage of the commenting service on the Playstore to convey messages to OVO managers. Hundreds of comments always appear every day, and this if not responded to will be a problem. The topic method of the Latent Dirichlet Allocation (LDA) model will be used to analyze the occurrence of user topics. Based on the 6-topic LDA model, we found that the trending topic was in topic 1, with a topic probability value of 0.235. Topic 1 mentions transaction difficulties with premium services with high OVO usage While the ease of transactions has the lowest total probability. The results of this topic can be used as a reference for OVO service providers to focus their performance on improving OVO applications. The impact of this research on service providers is to find out the topics discussed by OVO application users.
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