Track: Operations Management

Abstract

A finance company is a form of company that takes advantage of technological developments by creating fintech-based mobile applications to improve service quality. The existence of a mobile application accelerates the company's business operations, especially payment transactions. In 2019, the customer service queue at PT XYZ reached 350 queues per month. Meanwhile, the cashier queues to make payments an average of 3800 per month. The high level of queues, especially in payment transactions, can certainly have an impact on office effectiveness and efficiency. This study aims to analyze what constraints have caused the low number of fintech payment application users, how to map potential and non-potential customers in the use of fintech payment applications, and how the effectiveness of the use of fintech payment applications at PT XYZ. Through exploratory descriptive research, secondary data from literature studies are used and primary data from questionnaires and interviews are used. Determination of the sample using the method of accidential convenience sampling, and quota sampling with a number of respondents 100 people. Sampling is carried out for customers who have Android / iOS mobile phones that can access the fintech payment application. The descriptive analysis method uses the SPSS 23 application, and the k-means data mining analysis and the C 4.5 algorithm use the WEKA 3.7 application.

The results showed that the problem with the low use of the fintech payment application was because 60% of customers did not know the application, 40% of customers did not have a bank account so they could not make transactions on the fintech payment application and 67% of customers chose payment media through outlets / minimarkets even though they had to pay a fee. additional transactions. The results of clustering processing show that what distinguishes potential and non-potential customers is visible from education and work, while age and income can be seen from the dominant answers. To determine the effectiveness of using the application, a TAM (Technology Acceptance Model) is used which consists of trust, value for profit, convenience, social influence and intention to use. Data classification processing with the C4.5 algorithm shows that there are only 3 attributes that determine the effectiveness of using the Adiraku application, namely the intention to use, trust and ease of use.