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All Journal International Transactions on Education Technology (ITEE) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Adele Valerry
iLearning Incorporation
Computer Science & IT Control & Systems Engineering Education Library & Information Science Social Sciences Other

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Integration of IoT and Blockchain for Business Data Security Uki Hares Yulianti; Yul Ifda Tanjung; Untung Rahardja; Ninda Lutfiani; Adele Valerry
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i1.1040

Abstract

The background of this study is based on the growing reliance of businesses on digital data driven by digital transformation, which demands higher standards of data security and transparency. The IoT and Blockchain are recognized as key technologies that can address these issues, yet empirical research exploring their combined roles remains limited. The objective of this research is to examine the role of the IoT in strengthening business data security, the role of Blockchain in enhancing data transparency, and the effect of integrating both technologies on business data management. This study adopts a quantitative approach, with data gathered through questionnaires distributed to business practitioners who have implemented digital technologies. The data were analyzed using descriptive statistical methods and simple inferential analysis to identify relationships among the research variables. The results show that the IoT positively influences business data security through real time monitoring, while Blockchain improves data transparency and integrity through its immutable recording mechanism. Moreover, the integration of the Internet of Things and Blockchain produces a stronger impact on data security and transparency compared to their individual use. The study concludes that the adoption and integration of the Internet of Things and Blockchain provide effective strategies for organizations to enhance business data security and transparency, while also fostering stakeholder trust and supporting business sustainability in the digital era.
Measuring the Effect of Interdisciplinary Learning Factory Projects on Student Learning Outcomes Etty Puji Lestari; Adele Valerry; Lilik Sulistyowati
International Transactions on Education Technology (ITEE) Vol. 4 No. 2 (2026): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i2.1109

Abstract

The rapid development of interdisciplinary knowledge and industry-driven education models has encouraged higher education institutions to adopt more experiential and collaborative learning approaches. The growing demand for graduates who possess not only theoretical knowledge but also practical competencies, collaborative abilities, and innovation skills has driven universities to integrate real-world project environments into the learning process through Learning Factory models. This study aims to measure the effect of interdisciplinary Learning Factory projects on student learning outcomes in higher education environments. The research applies a quantitative approach using a survey-based method involving students who participated in interdisciplinary Learning Factory projects, with the collected data analyzed through statistical analysis techniques to examine the relationship between interdisciplinary project activities and learning outcomes. The findings reveal that interdisciplinary Learning Factory projects significantly improve student learning outcomes, particularly in the areas of problem-solving ability, critical thinking, collaboration, and the integration of theoretical and practical knowledge. Students who participate in interdisciplinary projects demonstrate higher engagement and stronger capability to address complex real-world problems compared to those involved in traditional learning environments. These findings suggest that the integration of interdisciplinary Learning Factory projects provides an effective learning strategy for enhancing student competencies and supporting the development of practical, collaborative, and innovation-oriented skills required in modern educational and industrial contexts.
Deep Learning Driven Big Data Architecture for Scalable Intelligent Network Threat Detection Sigit Anggoro; Palma Juanta; Ariesya Aprillia; Adele Valerry
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/vc0kmq21

Abstract

This study proposes a deep learning driven big data architecture designed to enable scalable and intelligent network threat detection in high volume traffic environments. Increasing network traffic volume and heterogeneity generated by enterprise systems, cloud services, and Internet of Things devices require more adaptive and intelligent security mechanisms beyond traditional signature-based approaches. This study aims to develop an intelligent threat-detection framework that leverages deep-learning models and big data analytics to enhance detection accuracy, scalability, and real-time response capabilities in large-scale network environments. A distributed big data architecture is integrated with advanced deep neural networks to process high-dimensional network traffic features, perform automated feature learning, and classify malicious activities using optimized training and validation strategies. The proposed framework is evaluated using benchmark intrusion detection datasets and simulated real-world network traffic scenarios to ensure robustness and generalizability. Experimental findings demonstrate that the proposed approach achieves superior detection accuracy, lower False-Positive Rates, and improved processing efficiency compared with conventional machine learning-based intrusion-detection systems. The integration of deep learning and big data analytics provides a scalable and adaptive solution for intelligent threat detection in computer networks, contributing to the development of next-generation cybersecurity systems capable of addressing evolving and sophisticated cyber attacks.
Blockchain Integration to Enhance Federated Learning Model Integrity Yane Devi Anna; Sherli Triandari; Sigit Anggoro; Ardirra Yolandita; Adele Valerry
Blockchain Frontier Technology Vol. 5 No. 2 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i2.929

Abstract

Federated Learning is a distributed machine learning approach that enables model training without transferring raw data, thereby preserving user privacy. To improve conciseness, overlapping explanations of FL’s privacy benefits across the Abstract, Introduction, and Literature Review have been consolidated, highlighting its importance in sensitive domains while removing redundancy. This allows greater emphasis on the study’s novelty, particularly the Smart Contract design featuring multi-layer verification and reputation checking mechanisms. Despite its advantages, FL faces significant challenges related to model integrity, including parameter manipulation, model poisoning attacks, and limited trust among participating nodes. This study explores the integration of blockchain technology to address these issues. Leveraging decentralization, immutability, and transparency, blockchain is used to validate model updates, record contributions, and manage node reputation. The study employs a literature review and technical architecture design for a blockchain-integrated FL system. The results indicate that blockchain implementation enhances the reliability and security of FL training, especially in low-trust environments, with strong relevance for healthcare, finance, and IoT applications.
Co-Authors Ardirra Yolandita Ariesya Aprillia Etty Puji Lestari Lilik Sulistyowati Ninda Lutfiani Palma Juanta Sherli Triandari Sigit Anggoro Sigit Anggoro Uki Hares Yulianti Untung Rahardja Yane Devi Anna Yul Ifda Tanjung