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Andree Emmanuel Widjaja
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International Transactions on Education Technology (ITEE)
Published by Pandawan Sejahtera Indonesia
ISSN : 29636078     EISSN : 29631947     DOI : https://doi.org/10.33050/itee
Core Subject : Social, Engineering,
Control & Systems Engineering Social Sciences
Computer Science/informatics, Circular Digital Economy, Computer engineering/computer systems, Software Engineering, Information Technology, Information Systems, Cyber Security, Data Science, Artificial Intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 58 Documents
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Blockchain Technology: Revolutionizing Data Integrity and Security in Digital Environments Maariz, Akhmad; Wiputra, Muhammad Aqil; Armanto, Muhammad Randika Dafa
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.435

Abstract

This study explores the transformative impact of blockchain technology on data integrity and security in digital environments. Through a comprehensive assessment of data integrity metrics across prominent blockchain networks, including Bitcoin, Ethereum, and Hyperledger Fabric, we unveil nuanced differences in immutability and reliability. Our security analysis delves into the cryptographic strength and resistance to unauthorized access, showcasing the outstanding security features of Hyperledger Fabric and Bitcoin, with Ethereum exhibiting commendable yet moderate security levels. The discussions underscore the multifaceted nature of blockchain technology, emphasizing the importance of selecting a platform aligned with specific use cases. Hyperledger Fabric and Bitcoin emerge as strong contenders for applications requiring high integrity and robust security, while Ethereum offers a reliable but moderate alternative. As blockchain technology continues to evolve, this study provides valuable insights for practitioners and researchers, guiding the strategic selection of blockchain platforms to harness their transformative potential in diverse digital environments.
Advancing E-commerce Smart-PLS as a Catalyst for Improved Online Shopping Services Alwiyah; Victorianda; Bennet, Daniel
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.540

Abstract

In the fast-paced world of e-commerce, the adoption of advanced methodologies and technologies is vital to address the increasing demands and challenges of online shopping services. This paper examines the transformative impact of Partial Least Squares Structural Equation Modeling (Smart-PLS) on e-commerce. Smart-PLS is a powerful tool that offers a robust framework for analyzing and enhancing various aspects of e-commerce, such as customer experience, service quality, and business performance. This study explores how Smart-PLS facilitates a data-driven decision-making approach, resulting in better user experiences, optimized supply chains, and improved business strategies. The abstract underscores the importance of Smart-PLS as a revolutionary tool in the e-commerce sector, enabling businesses to adapt, innovate, and succeed in the digital marketplace. It highlights the potential of Smart-PLS to shape the future of online shopping services and stresses the necessity of its adoption for maintaining competitiveness in the ever-changing e-commerce landscape.
Ethical Considerations in the Development of AI-Powered Healthcare Assistants Willson, Jett Lee; Nuche, Asher; Widayanti, Riya
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.566

Abstract

Advances in the field of artificial intelligence (AI) have led to the development of increasingly sophisticated health assistants that can provide support in diagnosis, treatment and general health management. However, as with the use of new technologies in the healthcare context, ethical considerations play an important role in the design, development, and implementation of AI-based health assistants. In this paper, we investigate various ethical considerations associated with the development of AI-based healthcare assistants. We explore issues such as the privacy and security of patient data, transparency and accountability in decision making, and the social and psychological impact of reliance on technology in the healthcare context. We also discuss efforts that can be taken to address these ethical challenges, including the development of appropriate regulatory guidelines, ongoing monitoring of system performance, and education and training for health professionals and end users. By seriously considering ethical aspects in the development of AI-based healthcare assistants, we hope to ensure that this technology can provide maximum benefit to patients while maintaining the ethical and moral values that underlie good healthcare practices.
Comparative Analysis of Scientific Approaches in Computer Science: A Quantitative Study Kruger, Felix; Queen, Zabenaso; Radelva, Ocean; Lawrence, Neil
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.567

Abstract

Computer Science is an interdisciplinary field drawing its foundations from a multitude of scientific and engineering domains. The study of Computer Science necessitates the integration of concepts from various fields, blending theoretical frameworks with practical applications. This dual approach, combining abstraction and design, allows for a comprehensive understanding of computational systems. Over the years, the historical evolution of Computer Science has witnessed the emergence of numerous sub-disciplines that increasingly communicate and overlap, driven by the advancement of communication technologies and the growing need for a holistic perspective in understanding complex systems. This interdisciplinary synergy is crucial in addressing contemporary challenges that are inherently multifaceted, requiring inputs from diverse scientific areas. As our world becomes more interconnected and dominated by intricate technological systems, the reductionist approach proves inadequate. Instead, a holistic view, which acknowledges and leverages the interdependencies among various scientific disciplines, becomes imperative. This paper explores the multifaceted nature of Computer Science, highlighting its foundational concepts, historical development, and the integration of theory and practice. It delves into how the convergence of different scientific fields within Computer Science fosters innovation and addresses complex real-world problems. By examining the interdisciplinary interactions and their implications, this study underscores the importance of a comprehensive approach in advancing the field of Computer Science and its applications in solving modern-day challenges.
Exploring Circular Digital Economy Strategies for Sustainable Environmental, Economic, and Educational Technology Wilson, Ashley; Kask, Rasmus; Ming, Li Wei
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.579

Abstract

This research explores the strategic implementation of digital economy principles to achieve environmental sustainability and economic prosperity, utilizing the SmartPLS method. In an era marked by heightened awareness of environmental challenges and the urgent need for sustainable economic solutions, the digital economy emerges as a promising and innovative approach. This study primarily focuses on the integration of digital technologies throughout the product and service life cycle, with the objectives of extending product longevity, minimizing waste, and enhancing resource efficiency. Through an extensive review of literature and multiple case studies, we delve into various dimensions of the digital circular economy. These dimensions include innovative business models, the pivotal role of consumers, the challenges encountered during implementation, and their overall impact on economic growth. The findings underscore the crucial importance of cross-sectoral collaboration and the formulation of supportive policies to unlock the full potential of this economic model. Moreover, this research highlights the synergies between digital transformation and circular economy practices, suggesting that their convergence can significantly drive sustainable progress in contemporary society. By presenting comprehensive insights into the digital economic cycle, this study aims to contribute to the discourse on sustainable innovation and provide a roadmap for policymakers, businesses, and researchers to foster a more sustainable and prosperous future.
Enhancing Circular Economy with Digital Technologies: A PLS-SEM Approach Williams, Jack; Prawiyogi, Anggy Giri; Rodriguez, Miguel; Kovac, Ivan
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.590

Abstract

This study investigates the transformative potential of the digital economy in fostering the principles of a circular economy. Utilizing the SmartPLS methodology, we explore key determinants that drive the transition towards a sustainable economic framework and assess their impacts on both environmental sustainability and economic resilience. Our analysis highlights that the integration of digital technologies, such as IoT, blockchain, and AI, within circular economy practices can significantly enhance resource efficiency, reduce waste, and promote sustainable economic growth. These technologies enable better tracking and management of resources, facilitating closed-loop systems that are essential for a circular economy. However, our findings also identify substantial challenges, including concerns over data security, digital divide, and unequal access to advanced technologies, which may hinder the equitable distribution of benefits. The study underscores the importance of an integrated policy approach that combines technological innovation with supportive regulatory frameworks to address these challenges and maximize the benefits of digital integration. Policymakers are encouraged to develop strategies that not only foster technological advancements but also ensure inclusive access and address security issues. This research provides comprehensive insights for stakeholders, including governments, businesses, and academia, in designing effective strategies and policies aimed at promoting a sustainable circular economy in the digital era. By aligning digital advancements with circular economy principles, we can pave the way towards achieving sustainable development goals and creating a resilient economic future.
Leveraging Data Utilization and Predictive Analytics: Driving Innovation and Enhancing Decision Making through Ethical Governance Br. Karo, Mestiana; Miller, Bella Pertiwi; Al-Kamari, Omar Arif
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.593

Abstract

Advances in information technology have fueled an exponential increase in the volume and diversity of data generated by organizations and individuals. In this era, Data Science has emerged as a crucial discipline for uncovering hidden patterns within data, thereby facilitating smarter decision-making processes. This paper presents a comprehensive and up-to-date overview of the challenges and opportunities in the application of Data Science, with a particular focus on the PLS (Partial Least Squares) analysis method. The PLS method, implemented through the SmartPLS application, synergizes partial path analysis with partial least squares techniques and has gained prominence as a preferred method for analyzing complex structural models within the field of Data Science. This study delves into the practical applications and benefits of PLS in handling diverse and intricate datasets, and also elucidates the potential obstacles encountered during its implementation. By examining the methodological strengths and addressing the challenges associated with PLS, this paper aims to provide valuable insights for researchers and practitioners seeking to leverage this method and the SmartPLS application for enhanced data analysis and informed decision-making.
Exploring the Frontier of Data Science: Innovations, Challenges, and Future Directions Ismail, Farid Bin; Xuan, Alvin Teo Zi; Rusilowati, Umi; Williams, James
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.594

Abstract

Data science, an interdisciplinary field, has profoundly transformed our understanding and utilization of data across diverse sectors such as healthcare, finance, marketing, and transportation. With the rapid advancements in computational power and the exponential growth of data from digital sources, sophisticated methodologies and tools have emerged, enabling deeper insights and more informed decision-making. This paper explores the latest innovations in data science, focusing on advancements in machine learning algorithms, big data technologies, and data visualization tools. It highlights the development of cutting-edge techniques that enhance predictive accuracy, optimize resource allocation, and improve operational efficiencies. Additionally, we address the key challenges faced by practitioners, including ensuring data quality and management, navigating ethical and privacy concerns, and bridging the skill gap within the workforce. By examining these aspects, the paper provides a comprehensive overview of the current state of data science and its implications for future research and application. The insights gathered aim to guide researchers and professionals in leveraging data science advancements while addressing the inherent challenges to maximize the potential benefits across various industries.
Harnessing Machine Learning to Optimize Renewable Energy Utilization in Waste Recycling Fernandez, Mateo; Faturahman, Adam; Santoso, Nesti Anggraini
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.595

Abstract

This research explores the application of Machine Learning techniques in utilizing renewable energy for the recycling process. As the world strives for sustainable solutions to meet energy needs and waste management challenges, this study investigates the integration of Machine Learning algorithms to optimize the production of renewable energy from waste recycling. By employing these algorithms, the research aims to enhance the efficiency and effectiveness of renewable energy generation while promoting environmentally responsible waste management practices. The study encompasses comprehensive data analysis from various recycling facilities, identifying energy consumption patterns and evaluating energy-saving opportunities. The findings reveal that applying Machine Learning can reduce energy consumption by up to 30%, increase recycling output, and decrease greenhouse gas emissions. These results highlight the potential benefits and challenges of implementing smart technology in the recycling process for renewable energy production. Furthermore, the research offers insights into how integrating Machine Learning can support long-term sustainability and significantly contribute to improved environmental management. Consequently, this study paves the way for a cleaner and more sustainable future, inspiring the broader adoption of innovative techniques within the waste management and renewable energy industries.
Enhancing Waste-to-Energy Conversion Efficiency and Sustainability Through Advanced Artificial Intelligence Integration Melinda, Vivi; Williams, Tane; Anderson, James; Davies, J George; Davis, Christopher
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.597

Abstract

Artificial intelligence (AI) has emerged as a pivotal tool in optimizing waste-to-energy conversion technology, addressing critical environmental issues while promoting sustainable energy sources. This study delves into the multifaceted role of AI in enhancing the efficiency and effectiveness of waste-to-energy processes. By leveraging AI, significant improvements can be achieved in automated waste sorting, process monitoring, and energy production forecasting. The integration of AI into these domains not only streamlines operations but also enhances the accuracy of data management, analysis, and processing. This results in a more efficient conversion of waste into energy, mitigating adverse environmental impacts and fostering sustainable energy practices. The research highlights the practical applications of AI in optimizing the entire waste-to-energy workflow, underscoring its potential to revolutionize this sector. Moreover, the study addresses the inherent challenges and discusses future prospects for AI implementation in waste-to-energy technologies. Through comprehensive analysis and case studies, the findings reveal that AI can significantly contribute to reducing environmental footprints and promoting a circular economy. This exploration provides valuable insights into how AI-driven innovations can lead to more sustainable and efficient waste management and energy production systems, paving the way for future advancements in this critical field.

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