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All Journal Bulletin of Electrical Engineering and Informatics ELINVO (Electronics, Informatics, and Vocational Education) Journal of Advanced Technology and Multidiscipline (JATM)
Ika Qutsiati Utami
Universitas Airlangga
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Customized moodle-based learning management system for socially disadvantaged schools Ika Qutsiati Utami; Muhammad Noor Fakhruzzaman; Indah Fahmiyah; Annaura Nabilla Masduki; Ilham Ahmad Kamil
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3202

Abstract

This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Fake account detection in social media using machine learning methods: literature review Kerrysa, Nalia Graciella; Utami, Ika Qutsiati
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5334

Abstract

With the rapid development of emerging technologies in the industrial revolution 4.0 or 5.0, social media has become one of the social environments to carry out social activities, both socializing and advertising. However, since it is an open platform by nature, cybercrime occurrence in social media is inevitable. Currently, more than a million fake accounts are existing on Instagram, Twitter, and Facebook, intending to increase followers, spread hoaxes, and spam. On one hand, it is difficult to manually eliminate these accounts on social media platforms. On the other hand, research on automatic fake account detection has been carried out for more than a decade. This study provides literature reviews aiming to deliver information about several methods and machine learning algorithms with the performances measured in identifying fake accounts on three well-known social media platforms: Twitter, Instagram, and Facebook.
Systematic review of mobile applications in learning features to support learners living with epilepsy disorders Utami, Ika Qutsiati; Hwang, Wu-Yuin; Syarief, Fathurrahman; Kalvin, Nicholas Juan
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8289

Abstract

We systematically review mobile health applications in supporting learners living with epilepsy disorders. There are two objectives of this study i.e., assessing the existing epilepsy-related apps and providing information about some features provided by the apps. In total, 18 of 47 mobile apps that meet the final criteria were reviewed using the Mobile Application Rating Scale (MARS). We found that more than half of the apps had below-average quality and most offered only a few distinct functionalities. Six of them were deemed high quality since they met all standard criteria. In terms of self-management features, we identified several important features such as the provision of a seizure calendar (14/18, 78%), report generation (5/18, 28%), adding individual seizure occurrence and causes (9/18, 50%), and emergency alert (6/18, 34%). The majority of the apps included medication tracker (12/18, 67%), expert consultation (6/18, 34%), and educational features (10/18, 56%). Moreover, 40% of included apps have considered self-efficacy features by providing analytical support for seizure frequency, duration, occurrence distribution, and analysis. This research can make in-person support more feasible for epilepsy learners so that it helps families, caregivers, and educators to easily manage the risk and perform continuous aid. This research can also be the basis for developing more patient-centered software for epilepsy management.
Design and Development of Interactive Moodle using Design Thinking to Support Online Learning Abdillah, Yan Amal; Utami, Ika Qutsiati; Setiyawan, Wahyu Beny Mukti; Pratama, Mochammad Rizal; Afani, Indra Resya; Pramesti, Ari Yohana
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.75255

Abstract

Moodle is an open-source learning management system (LMS) or e-learning platform that is designed to help educators create online courses with various features to facilitate learning. Interest in this research area has increased significantly in recent years, leading to an increasing number of schools employing this LMS. In the early stages of the development process, schools need to gain insights into user needs and generate new ideas for designing and developing interactive LMS. In this light, Design Thinking (DT) is viewed as an important approach. This study aimed to design and develop an online learning system based on Moodle to facilitate online learning for elementary school students. The objectives were to (1) design a system diagram, system architecture, and functional structure for facilitating online learning and (2) develop an interactive LMS using Moodle. The research methodology was conducted following the DT process. An evaluation was made to assess the usability of the system using the Computer System Usability Questionnaire (CSUQ). Across dimensions such as system quality, information quality, and interface quality, scores ranged between 5 and 7, highlighting a positive user perception across various aspects of the evaluation. These findings, therefore, offer practical guidance for educational stakeholders in adopting and promoting Moodle-based LMS.
Student's Behavior Clustering based on Ubiquitous Learning Log Data using Unsupervised Machine Learning Utami, Ika Qutsiati; Hwang, Wu-Yuin; Ningrum, Ratih Ardiati
Journal of Advanced Technology and Multidiscipline Vol. 3 No. 1 (2024): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v3i1.55572

Abstract

Online learning is the source of data generation related to learner's learning behaviors, which is valuable for knowledge discovery. Existing research emphasized more on an understanding of student's performance and achievement from learning log data. In this study, we presented data-driven learning behavior clustering in authentic learning context to understand students' behavior while participating in the learning process. The objective of the study is to distinguish students according to their learning behavior characteristics and identify clusters of students at risk of unsuccessful learning achievement. Learning log data were collected from ubiquitous learning applications before conducting Exploratory Data Analysis (EDA) and cluster analysis. We used partitional clustering using K-means algorithm and hierarchical clustering based on the agglomerative method to improve clustering strategies. The result of this study revealed three different clusters of students supported by data visualization techniques. Cluster 1 comprised more students with active learning behavior based on the total logs, total problems posed, and the total attempts in fraction operation and simplification. Students in clusters 2 and 3 had a higher attempt at problem-solving instead of problem-posing. Both clusters also focused on fraction's conceptual understanding. Knowledge discovery of this study used real data generated from ubiquitous learning application namely U-Fraction. We combined two different types of clustering method for delivering more accurate portrait of a student's hidden learning behaviors. The outcome of this study can be a basis for educational stakeholders to provide preventive learning strategies tailored to a different cluster of students.
Co-Authors Abdillah, Yan Amal Afani, Indra Resya Annaura Nabilla Masduki Hwang, Wu-Yuin Ilham Ahmad Kamil Indah Fahmiyah Kalvin, Nicholas Juan Kerrysa, Nalia Graciella Muhammad Noor Fakhruzzaman Ningrum, Ratih Ardiati Pramesti, Ari Yohana Pratama, Mochammad Rizal Syarief, Fathurrahman Wahyu Beny Mukti Setiyawan