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All Journal ADI Journal on Recent Innovation (AJRI) International Journal of Engineering, Science and Information Technology International Transactions on Education Technology (ITEE)
Natasya, Ersa Aura
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The Effect of Technology Training on Increasing MSME Productivity: Case Analysis of Digital Training Programs for Local Craftsmen Lukita, Chandra; Purnama, Ika Yuni; Rahardja, Untung; Natasya, Ersa Aura; Sanjaya, Yulia Putri Ayu
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1087

Abstract

This research aims to explore the impact of technology training on increasing the productivity of micro, small and medium enterprises (MSMEs), focusing on local artisans. The Partial Least Squares structural analysis method (PLS-SEM) tests the proposed hypothesis based on survey data from MSMEs participating in digital training programs. The research results show that active participation in technology training programs significantly increases the application of technology in MSME business operations. Applying this technology will then have a positive impact on improving the productivity of MSMEs. Additionally, consistency in construct measurement, such as reliability and validity, is vital in explaining variation in the dependent variable. These findings provide an essential contribution to understanding the role of technology in increasing the productivity of MSMEs and highlight the importance of consistency in construct measurement in the context of this research.
Enhancing Adaptive Learning Environments in Learning Factories through Artificial Intelligence Natasya, Ersa Aura; Lestari Santoso, Nuke Puji; Lukita Pasha; Hua, Chua Toh; Carlos Perez
International Transactions on Education Technology (ITEE) Vol. 4 No. 1 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i1.957

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly trans- formed educational paradigms, particularly in adaptive learning environments where real-time personalization and intelligent feedback are essential. This study aims to explore how AI-driven mechanisms can enhance adaptive learning within learning factory environments by utilizing data analytics to personalize learning processes and optimize instructional delivery. Employing a quantita- tive research design, the data collection process involved distributing question- naires to 200 university students enrolled in AI-supported learning factory pro- grams. From this distribution, 120 valid responses were successfully obtained and analyzed, consisting of 80 students and 40 instructors across three universi- ties, representing the final usable dataset for this study. Statistical analysis was performed using regression and correlation models to assess the impact of AI- based adaptivity on learning performance, engagement, and cognitive retention. The findings reveal that AI integration within learning factories leads to sig- nificant improvements in learner adaptability, interaction efficiency, and overall academic achievement. The adaptive AI models dynamically adjusted learning content based on individual performance metrics, resulting in higher engage- ment rates and enhanced skill mastery compared to traditional non-AI-based environments. The outcomes confirm that AI can function as a critical enabler of responsive and data-driven education by bridging theoretical and practical as- pects of industrial learning. This research underscores the transformative poten- tial of Artificial Intelligence in reshaping adaptive learning environments within learning factories, emphasizing the need for further development of AI systems that prioritize personalization, continuous assessment, and the seamless integra- tion of human and machine intelligence
Designing Ethical AI Systems for Sustainable Technology Development Zainol, Zuraidah; Brotosaputro, Goenawan; Chen, Shih Chih; Natasya, Ersa Aura
ADI Journal on Recent Innovation Vol. 6 No. 2 (2025): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v6i2.1205

Abstract

Co-Authors Carlos Perez Chen, Shih Chih Goenawan Brotosaputro Hua, Chua Toh Lestari Santoso, Nuke Puji Lukita Pasha Purnama, Ika Yuni Sanjaya, Yulia Putri Ayu Untung Rahardja Zainol, Zuraidah