8ISC Proceedings: Technology

Analysis of the Satisfaction Level of Gojek (GoFood) Application Users in Rawalumbu, Bekasi City Using the PIECES Framework During Pandemic Period

Patricia Lorraine Septina, Jay Idoan Sihotang — Thu, 10 Feb 2022 00:00:00 +0000

The problems studied arise from users regarding the facilities used and obtained from the Gojek application. Many aspects are taken into consideration by consumers in choosing and deciding which application to use. An important aspect that is considered by consumers is satisfaction with service quality in meeting consumer needs and desires, which are still uncertain. The data collection technique used by the researcher is a questionnaire. The population in this study are users of the Gojek application in Rawalumbu, Bekasi City, with a sample of 100 respondents using the Lemeshow formula. The PIECES Framework method provides tangible results on the effectiveness of the Gojek (GoFood) application system by analyzing the Performance, Information, Economics, Control & Security, Efficiency, and Service variables. Calculation of the results of the questionnaire using a Likert Scale to measure the respondent’s perception or opinion of a condition is then made in the form of a percentage using the Customer Satisfaction Index. The percentage of satisfaction level for each variable, namely, the Performance variable gets an index value of 86.40%, the Information variable gets an index value of 85.80%, Economics gets an index value of 84.60%, the Control and Security variable gets an index value of 83.00%, the variable Efficiency gets an index value of 88.40%, and Service gets an index value of 86.40%. So the results of this study are the total average level of satisfaction from all domains, namely getting an index value of 85.76% (Very Satisfied).

Keywords: Gojek Application, Gofood, PIECES Framework, Customer Satisfaction Index

User Satisfaction Analysis on Microsoft Teams and Google Classroom as E-Learning Media Using the E-ServQual Method

Thu, 10 Feb 2022 00:00:00 +0000

The development of technology opens up great opportunities for the education industry from Elementary School, Junior High School, Senior High School to College so that the learning system can be done online using the E-Learning or Electronic learning method. E-Learning is carried out to reduce face-to-face activities due to the Covid-19 pandemic so that every activity that will be carried out is shifted online. E-Learning is developed through mobile applications and websites that are bridged by information technology, and the E-Learning process can use platforms such as Microsoft Teams and Google Classroom, which are online classrooms that can facilitate lecturers and students in communication. To assess to effectiveness of both platforms are used as a medium of learning and to see which better platform to be used as a medium of online learning, then do research by using E-ServQual or Electronic Service Quality as a measure to compare the level of user satisfaction in when using Microsoft Teams and Google Classroom that is used in the process of E-Learning. After the researchers conducted an analysis of the comparison of user satisfaction by looking at the level of effectiveness owned by Microsoft Teams and Google Classroom as a medium of online learning used by students of the Faculty of Computer Science and Unklab Business School that exist in Klabat University, the result found is that the use of Google Classroom is more effective to use on online learning in comparing Microsoft Teams, which means that students are more satisfied in compare or comfortable when using Google Classroom than Microsoft Teams when learning online.

Keywords: E-Learning, Microsoft Teams, Google Classroom, E-ServQual

Design of End-to-end Student Research Administration Application using Rapid Prototyping Method in Universitas Advent Indonesia

Andrew Fernando Pakpahan — Thu, 10 Feb 2022 00:00:00 +0000

In this study, an end-to-end student thesis research administration application is proposed, which provides a comprehensive solution to the problems of the thesis administration process at Universitas Advent Indonesia, especially in the midst of the current pandemic. The rapid prototyping method is used to ensure that the resulting application can be developed quickly while receiving input from users to improve the application according to user requirements. The development of the application was started by conducting interviews with application users, followed by making application prototypes. The application was developed using web-based technologies (PHP and VueJS); thus, the application can be accessed using a web browser. Afterward, an integration was carried out between the developed prototype and the existing campus academic system. The proposed application provides the following features: research (thesis) title submission and approval, research guidance recording, proposal seminar approval, proposal seminar scheduling, proposal seminar grade input, proposal seminar revision records, proposal seminar revision completion approval, final defense approval, final defense scheduling, final defense’s grade input, final defense revision records, final defense revision completion approval, and final thesis book bundle completion approval. Therefore, the development of the application provides an end-to-end system that is able to help the student thesis research administration process in Universitas Advent Indonesia.

Keywords: information system, web application, research administration

A Comparative Study on Hepatitis C Predictions Using Machine Learning Algorithms

Fergie Joanda Kaunang — Thu, 10 Feb 2022 00:00:00 +0000

Hepatitis C virus (HCV) is known to be the major cause of chronic liver disease. Based on research, HCV has caused more than 100.000 cases of liver cancer per year. This virus has become the cause of at least 280.000 deaths. To diagnose HCV, it takes at least two different tests, namely serological assays and molecular tests, which are quite costly and complex. With Machine Learning technology, the diagnosis of any disease or virus can be made by detecting different patterns or relationships. Therefore, this study aims to predict the Hepatitis C virus using different machine learning algorithms and find out the best model for the classification of Hepatitis C disease. Furthermore, this study shows some visualizations to find out the relationships between attributes. We used different machine learning algorithms, namely K-Nearest Neighbour, Support Vector Machine, Random Forest, Neural Network, Naïve Bayes, and Logistic Regression. The performance of those different machine learning algorithms was evaluated using four different metrics, which are classification accuracy, precision, recall, and F-1 score. The classification accuracy results are 96.5%, 96.7%, 97.3%, 97.1%, 96%, 97.9% each for k-NN, SVM, RandomFores, Neural Network, Naïve Bayes and Logistic Regression. Based on the results, each model showed high performance, but Logistic Regression performs the best result. With the results conducted by this study, it is hoped that it can help the diagnosis process of HCV based on laboratory data. However, it is important to communicate the shortcomings and some possible improvements for each model.

Keywords: Machine Learning, Predictions, Hepatitis C Virus

Users’ Behavior of Identity Protection and Online Data Security Management

Noah Anburaj Balraj, Stanislav Kirilov — Thu, 10 Feb 2022 00:00:00 +0000

The objective of this study was to understand the user’s IT knowledge to account and data management practices related to personal, social, and business. Internet is global and the vulnerability is global too. The Internet has become part of the people who are engaged with it. The purpose of the study was to learn the level of users’ IT knowledge through the account, identity protection, and online data management practices to understand the relationship and design the needed programs to enhance their knowledge for better protection. With the increase in users of mobile phones, laptops, desktops emerge issues and development measures. The identified gap was between the users’ IT knowledge and their practices on account and data management. This gap was identified to help design programs to help the users to develop strategies through various alternatives to prevent identity and data theft. The population of data was collected in AIU for strengthening in protecting identity theft and data theft of our faculty members, staff members, students, and other stakeholders. Two samples Chi-Square was used for the analysis of data for the rejection or acceptance of null or alternate hypotheses based on critical value using Microsoft excel. The analysis was individually done for all the 27 factors considered in this research. Results show eight factors had a significant relationship, and 19 factors do not significantly relate between users’ IT knowledge and data management and practices. A follow-up study will be conducted on the sub-ordinate level of users’ behavior and practice of identity theft and data protection.

Keywords: User experience, identity protection, online data management and practices