Claim Missing Document
Check
Articles

Found 12 Documents
Search

Strengthening Civic Character through Community Service Based on Digital Local Wisdom Gandhi, Iswara; Nuraeni, Rani; Rodriguez, Anthony; Anita, Tiurida Lily; Lesmana, Rosa
ADI Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): ADI Pengabdian Kepada Masyarakat
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/adimas.v6i1.1307

Abstract

The digital era, marked by disruption and globalization, poses challenges to strengthening civic character, particularly in preserving values of mutual cooperation, social responsibility, and environmental awareness, with universities playing a strategic role through community service programs rooted in local wisdom and digital adaptation. This community service aims to analyze the effectiveness of the Digital-Based Environmentally Concerned Village program in strengthening civic character among the people of Sumberjaya Village, Sukabumi. The program was implemented in a rural agrarian village community, where most residents work as farmers and small traders. The community strongly upholds traditions of collective work and environmental care, such as joint land cleaning and deliberation meetings. These practices were contextualized into digital initiatives to encourage broader participation. A qualitative case study approach was employed through participatory observation, in-depth interviews with students, residents, and local leaders, and analysis of documents and digital artifacts produced during program implementation. The findings indicate increased nationalism and patriotism through the internalization of local values, the development of digital ethics and social responsibility in media use, enhanced collaboration and mutual cooperation, and greater environmental concern, with the integration of local wisdom and digital technology serving as a key catalyst that expanded program reach and participation. The synergy between tradition and digital innovation is an effective strategy to foster adaptive, caring, and empowered citizens, and it can be replicated in other communities with adjustments to their respective local wisdom.
SWOT Analysis of AI-Based Learning Recommendation Systems for Student Engagement Nuraeni, Rani; Hardini, Marviola; Parker, Jonathan; Ilham, Muhammad Ghifari
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.894

Abstract

This study presents a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) of AI-based learning recommendation systems for students. These innovative systems hold significant potential in supporting Sustainable Development Goal (SDG) 4 Quality Education by personalizing learning pathways, enhancing access to resources, and boosting student engagement. Their primary strengths include increased learning efficiency, adaptive content delivery, and instant feedback mechanisms. Nevertheless, weaknesses such as potential algorithmic bias, data privacy concerns, and over reliance on technology warrant careful consideration. Emerging opportunities encompass expanding educational access for underserved populations, facilitating lifelong learning, and integrating diverse educational platforms. However, threats like the digital divide and the need for robust ethical guidelines must be addressed to ensure equitable access. This analysis underscores the necessity of a balanced approach in developing and deploying these AI systems, maximizing their educational benefits while mitigating risks to achieve more inclusive and equitable quality education for all. Quantitatively, the synthesis of reviewed studies reveals that adaptive AI-based recommendation systems improve student engagement by up to 18% and content relevancy by approximately 22% compared to conventional systems. Moreover, the SWOT analysis indicates that the strength to threat ratio (S/T) exceeds 2.1, implying that institutional readiness and technological innovation significantly outweigh identified implementation risks. These findings confirm the robust potential of AI-LRS in higher education.