Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal International Transactions on Education Technology (ITEE) Blockchain Frontier Technology (BFRONT)
Carlos Perez
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education Library & Information Science Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimizing Agricultural Supply Chain Transparency in Indonesia through Blockchain and Smart Contracts Konita Lutfiyah; Harlis Setiyowati; Cicilia S Bangun; Amroni; Carlos Perez
Blockchain Frontier Technology Vol. 5 No. 1 (2025): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i1.740

Abstract

This research explores the integration of blockchain technology within Smart Farming 4.0 to improve efficiency, transparency, and sustainability in Indonesia’s agricultural sector. the challenge of transforming traditional agriculture into a modern, data-driven system remains significant, especially for smallholder farmers. By leveraging blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI), In this study, investigates how these technologies can opti- mize supply chains, enhance product traceability, and reduce distribution costs. Employing a mixed-method approach, data were collected through interviews with farmers utilizing blockchain-based systems and surveys targeting milenial farmers. The findings reveal that blockchain significantly improves trace- ability and logistics efficiency, while highlighting key barriers such as low digital literacy and limited access to technology. The results emphasize the importance of government and private sector collaboration in providing incentives, digital literacy training, and infrastructure support. Ultimately, the study offers practical insights into accelerating digital transformation in agriculture and promoting sustainable food systems through technological innovation.
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
Co-Authors Amroni Cicilia S Bangun Harlis Setiyowati Hua, Chua Toh Konita Lutfiyah Lestari Santoso, Nuke Puji Lukita Pasha Natasya, Ersa Aura