Army Justitia
Bachelor Of Information Systems Study Program, Faculty Of Science And Technology, Universitas Airlangga, Surabaya-Indonesia

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Journal : Journal of Information Systems Engineering and Business Intelligence

Customer Satisfaction Analysis of Online Taxi Mobile Apps Army Justitia; Rini Semiati; Nadhila Ramadhini Ayuvinda
Journal of Information Systems Engineering and Business Intelligence Vol. 5 No. 1 (2019): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.945 KB) | DOI: 10.20473/jisebi.5.1.85-92

Abstract

Background: High number of complaints that have been filed about the performance of online taxi services has prompted research on customer satisfaction factor analysis. Substantial research has addressed customer satisfaction factors in online taxi services, but none of them investigated the satisfaction in using the mobile apps.Objective: This study aims to find out the level of customer satisfaction and customer satisfaction factors in the online taxi mobile app services.Methods: This study is quantitative in nature, using questionnaires and purposive sampling method. The Customer Satisfaction Index (CSI) and Important-Performance Analysis (IPA) were used to determine the customer satisfaction factors, with the variables being route detection, connection, interaction, content, and service quality; as well as customer satisfaction, customer’s complaint, and customer loyalty. The data was processed using SPSS software.Results: The results showed that the level of customer satisfaction was 76.117% and fell into Cause of Concern category. This means that the system performance did not meet customer expectations. The results also showed that the best three factors in online taxi mobile apps are route detection, interaction, and content quality. Meanwhile, the factors that caused customer dissatisfaction were connection and service quality. The variables that led to satisfaction need to be maintained and the variables that did not were in Quadrant 1.Conclusion: The customer satisfaction was low so it is advisable that the companies immediately take an action to improve their performance and revise their strategic planning. In doing so, they must prioritize the attributes which have the biggest gap because these are the ones that will improve customer satisfaction.
Tool for Generating Behavior-Driven Development Test-Cases Indra Kharisma Raharjana; Fadel Harris; Army Justitia
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.27-36

Abstract

Background: Testing using Behavior-Driven Development (BDD) techniques is one of the practices of Agile software development. This technique composes a test-case based on a use case scenario, for web application acceptance tests.Objective:  In this study, we developed a tool to generate test case codes from BDD scenario definitions to help and facilitate practitioners to conduct testing.Methods: The generated test case code is made according to the codeception framework format so that it can be directly executed by the tester. The procedure is performed as follows:  map the correlation of the language used in BDD (gherkin language) and the code syntax of the test code in the codeception framework, designed the GUIs in such a way that users can easily transform the Use Case Scenario, built the tool so that it can generate test cases codes. Evaluation is done by gathering respondents; ask to run the application and gathering feedback from respondents.Results: This tool can generate a codeception test-case file based on the BDD scenario. Generated test cases can be directly used on codeception tools. The results of the evaluation show that the tools can help entry-level programmers in developing automated tests.Conclusion: The tool can help user especially entry-level programmers to generate BDD test-case and make easy for the users for testing the web applications.
Students Activity Recognition by Heart Rate Monitoring in Classroom using K-Means Classification Hadi Helmi Md Zuraini; Waidah Ismail; Rimuljo Hendradi; Army Justitia
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.46-54

Abstract

Background: Heartbeat playing the main roles in our life. With the heartbeat, the anxiety level can be known. Most of the heartbeat is used in the exercise. Heart rate measurement is unique and uncontrollable by any human being.Objective: This research aims to learn student’s actions by monitoring the heart rate. In this paper, we are measuring the student reaction and action in classroom can give impact on teacher’s way of delivery when in the teaching session. In monitoring, student’s behavior may give feedback whether the teaching session have positive or negative outcome.Methods: The method we use is K-Means algorithm. Firstly, we need to know the student’s normal heartbeat as benchmark. We used Hexiware for collecting data from students’ hear beat. We perform the classification where K is benchmark students’ heartbeat. K-Means algorithm performs classification of the heart rate measurement of students.Results: We did the testing for five students in different subjects. It shows that all students have anxiety during the testing and presentation. Its consistency because we tested 5 students with mixes activities in the classroom, where the student has quiz, presentation and only teaching.Conclusion: Heart rate during studying in the classroom can change the education world in improving the efficiency of knowledge transfer between student and teacher. This research may act as basic way in monitoring student behavior in the classroom. We have tested for 5 students. Three students have their anxiety in classroom during the exam, presentation, and question. Two students have normal rate during the seminar and lecturer. The drawback, Hexiware is capturing average of ten minutes and tested in different classes and students. In future, we need just measure one student for all the subjects and Hexiware need to configure in one minute.