Abstract :

The development of online loan companies in Indonesia has brought both positive and negative impacts on society. From 2018 to February 2023, the Financial Services Authority (OJK) has shut down a total of 4,567 illegal online loan platforms. The number of victims involved in illegal online loan cases in Indonesia continues to rise each year.

The research aims to conduct semantic analysis to establish clear metrics that can assist online loan customers in choosing the right platform based on their individual needs. The study’s outcome is expected to serve as an alternative source of information for online loan customers when selecting a suitable application.

The research employs text mining, specifically semantic analysis using the Wmatrix5 application. Data is gathered through crawling reviews and comments from customers of legal online loan companies available on the Google Playstore.

The analysis reveals industry focused aspects of applications and platforms, emphasis on the loan process, efforts to provide convenience and customer satisfaction, and attention to security and trust. Additionally, the analysis exposes negative aspects, including ambiguity in loan limits, the importance of data security, potentially detrimental debt collection practices, and the possibility of hidden fees.

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Keywords :

Customer Experience, Customer Trust, Online Loan, Semantic Analysis.

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