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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Community Engagement Journal Innovation in Information and Computer Technology
Muhammad Ridho Ardiansyah
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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Lecturer  Mentoring  on  SINTA,  Garuda,  and  Google  Scholar  at STIKES Abdurrahman and ITB Bina Sriwijaya Palembang Muhammad Ridho Ardiansyah; Mahmud; Indah Rahma Sari; Hendriansyah; Martini; Nabila Kintan Oktadinna
Indonesian Journal of Community Engagement Vol. 3 No. 1 (2026): (January) Indonesian Journal of Community Engagement
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/ijce.v3i1.101

Abstract

Mentoring activities in managing SINTA, Garuda, and Google Scholar accounts represent a strategic effort to improve the quality of lecturer publication governance and the performance of higher education institutions. This study aims to describe the process and outcomes of mentoring activities related to the management of SINTA, Garuda, and Google Scholar accounts for lecturers at STIKES Abdurahman Palembang and Institut Bina Sriwijaya Palembang. The study employed a qualitative approach with a descriptive design. Data were collected through observation, documentation, and field notes during the implementation of the mentoring activities. The mentoring was conducted through material presentations, hands-on practice, and individual assistance, focusing on profile updates, publication synchronization, metadata improvement, and the creation of global researcher identifiers such as ORCID and Web of Science. The results indicate a significant improvement in institutional publication performance, particularly at Institut Bina Sriwijaya Palembang, as evidenced by an increase in the number of verified authors, SINTA Overall Score, SINTA 3-Year Score, and SINTA productivity. Meanwhile, at STIKES Abdurahman Palembang, the mentoring activities contributed to improving the accuracy and validity of lecturer publication data through cross-platform account synchronization. Overall, these mentoring activities proved effective in enhancing the quality of lecturer publication management, increasing participant engagement, and supporting sustainable improvements in academic performance and institutional visibility.
Large Language Model-Based Intelligent Tutoring System for Programming Education Herli Cecilia; Muhammad Ridho Ardiansyah
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.v2i1.115

Abstract

The rapid advancement of artificial intelligence, particularly large language models (LLMs), has significantly transformed the landscape of digital learning environments. In programming education, students often face difficulties such as limited instructor availability, delayed feedback, and insufficient personalized guidance during the learning process. Intelligent Tutoring Systems (ITS) have been widely proposed as a solution to provide adaptive and individualized learning support. However, traditional ITS architectures often rely on predefined rule-based models that limit their scalability and contextual understanding. This study proposes a large language model-based intelligent tutoring system designed to enhance programming education through adaptive learning support, automated feedback, and natural language interaction between students and the system. The proposed framework integrates LLM capabilities with a tutoring architecture that supports real-time code explanation, debugging assistance, and concept clarification tailored to individual learner needs. The system leverages prompt engineering and retrieval mechanisms to improve response relevance and pedagogical effectiveness. The results demonstrate that integrating LLM technologies into tutoring systems can improve students’ learning engagement, programming performance, and problem-solving abilities. Furthermore, the proposed approach enables scalable educational assistance that can support learners in environments with limited teaching resources. The findings suggest that LLM-based tutoring systems have strong potential to become an effective solution for personalized programming education in modern digital learning ecosystems.
Generative AI-Based Adaptive Learning Model for Personalized Higher Education Dimas Ardhana; Muhammad Ridho Ardiansyah; Yuliza Aryani
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.v2i1.121

Abstract

The rapid development of Artificial Intelligence (AI) technologies has significantly transformed the landscape of higher education, particularly in the context of personalized learning. Traditional learning systems often apply uniform instructional methods that fail to address individual differences in students' learning pace, preferences, and cognitive abilities. Consequently, there is a growing need for adaptive learning systems capable of providing personalized educational experiences. Generative Artificial Intelligence (Generative AI) has emerged as a promising technology that can dynamically generate educational content, feedback, and learning pathways tailored to individual learners. This study proposes a Generative AI-Based Adaptive Learning Model designed to support personalized learning in higher education environments. The model integrates machine learning algorithms, learning analytics, and generative AI techniques to analyze student learning behavior and automatically generate adaptive learning materials and recommendations. The research adopts a design science research methodology (DSRM) to develop and evaluate the proposed model through conceptual design and system architecture analysis. The results indicate that integrating generative AI into adaptive learning systems can enhance learning personalization, improve student engagement, and support instructors in delivering more efficient and scalable educational experiences. Furthermore, the proposed model provides a flexible framework that can be implemented in various digital learning platforms and learning management systems. The findings of this study contribute to the development of intelligent learning environments that leverage generative AI to improve the quality of higher education in the digital era.
Co-Authors Dimas Ardhana Hendriansyah Herli Cecilia Indah Rahma Sari Mahmud Martini Nabila Kintan Oktadinna Yuliza Aryani