ANALYSIS OF SENTIMENT ON USE OF MOBILE BANKING AT BANK SUMUT USING THE NAIVE BAYES METHOD - Haposan Arthur Fransiskus Turnip; Nazaruddin; Sugih Arto Pujangkoro (International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS), 2024)
Mobile applications have several other limitations that other types of software do not have, such as mobile context, connections, small screens, different resolutions, limited power and capabilities and different data entry methods, so that in evaluating usability factors they cannot be equated with software on In general, this research aims to analyze the causal relationship used to explain the evaluation of the usefulness of using Bank Sumut's mobile banking by using sentiment analysis to determine customer satisfaction with Bank Sumut's mobile banking. Based on the results of testing the sentiment of the North Sumatra mobile application via sentiwordnet from 200 review data, it was found that the North Sumatra mobile application still has several problems such as complaints about the application being slow to respond. Complaints were also submitted for device connection processes that experienced errors. So it can be concluded that users want their needs to be heard and improvements are made based on user feedback to evaluate the usability factors of mobile applications. Based on the test results, the sentiment level needs to be considered for evaluating usability factors because with the sentiment level it is possible to identify the shortcomings of a mobile application based on certain usability factors. So it can be concluded that Bank Sumut can take advantage of the sentiment of North Sumatra mobile application users to increase response times in handling complaints or identify improvements needed by users, such as ease of non-cash payment transactions. Based on the test results, it can be concluded that when conducting sentiment analysis to get the best evaluation results, it is best to use a support vector machine with an accuracy value of up to 80% to get better usability evaluation results.