Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Indonesian Journal of Community Engagement Journal Innovation in Information and Computer Technology
Amelia Anggraini
Institut Teknologi dan Bisnis Bina Sriwijaya Palembang, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Implementation of Canva Training to Improve Digital Design Skills of Students and Teachers at SMA Nusa Bangsa Palembang Muh Ridho Ardiansyah; Amelia Anggraini; Indah Rahma Sari; Andri Saputra; Mahmud
Indonesian Journal of Community Engagement Vol. 3 No. 2 (2026): (May) Indonesian Journal of Community Engagement
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/ijce.v3i2.135

Abstract

The development of digital technology requires students and teachers to have digital design skills that can support learning activities, school publication, and visual communication. Canva is one of the design platforms that is easy for beginners to use because it provides templates, visual elements, text editing features, color arrangement tools, and design export options. This activity aimed to improve the digital design skills of students and teachers at SMA Nusa Bangsa Palembang through practice-based Canva training. The method used a descriptive community service approach consisting of preparation, Canva introduction, poster design demonstration, participant practice, mentoring, poster output review, and reflection. The training was conducted at SMA Nusa Bangsa Palembang and involved students and teachers as participants. The training materials focused on creating school activity posters using Canva, including template selection, text editing, adding design elements, arranging color composition, and downloading the final design. The results showed that participants were able to follow the training process and produce a school activity poster containing important information such as the activity title, schedule, requirements, contact information, and supporting visual elements. This activity provided practical experience for participants in creating school publication media that were neater, more attractive, and more communicative. Therefore, Canva training can be considered a simple and applicable strategy to improve students’ and teachers’ digital design skills in the school environment.
Machine Learning-Based Learning Analytics for Predicting Student Engagement in Online Learning Deva Rahma aulia; Amelia Anggraini; Irwansyah
Journal Innovation in Information and Computer Technology Vol. 2 No. 1 (2025): (January) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i1.116

Abstract

The rapid adoption of online learning environments has generated massive amounts of educational data through Learning Management Systems (LMS). However, one of the major challenges in online education is maintaining and predicting student engagement, which significantly affects learning outcomes and course completion rates. This study proposes a machine learning-based learning analytics approach to predict student engagement in online learning environments. Data are collected from student interaction logs within a Learning Management System, including activity frequency, time spent on learning materials, forum participation, and assessment performance. Several machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and Logistic Regression, are utilized to develop predictive models. The experimental results demonstrate that machine learning models can effectively predict student engagement levels and identify students at risk of disengagement. The findings provide insights for educators and institutions to design adaptive interventions and improve the quality of online learning environments. This research contributes to the growing field of learning analytics by demonstrating how machine learning techniques can enhance engagement prediction and support data-driven decision making in education.
Co-Authors Andri Saputra Deva Rahma aulia Indah Rahma Sari Irwansyah Mahmud Muh Ridho Ardiansyah