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Breakthroughs Information Technology
Published by Generate Digital Publishing
ISSN : -     EISSN : 31098495     DOI : 10.70764/gdpu-bit
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
BIT is an open-access journal which means that all content is freely available at no cost to the user or the institution. The scope of the journal includes empirical and theoretical articles relating to all aspects of information science, engineering and technology. It focuses on the biggest breakthroughs in the technology arena, with particular concentration on accelerating principles, concepts and applications, informatics and cultural informatics, high-performance computing, and reports on the continuous evolution of information science and technology as a whole.
Arjuna Subject : Umum - Umum
Articles 10 Documents
 1 
Quantum-Ai Integration: A Systematic Review of Algorithms, Hardware Efficiency and Secure Applications Muhammad Ubaidurrohman
Breakthroughs Information Technology Vol 1 No 1 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(1)-01

Abstract

Objective: This research aims to evaluate and synthesise the development of artificial intelligence (AI) technologies integrated with quantum computing, especially regarding processing efficiency, hardware energy efficiency, and digital communication security.Research Design & Methods: This research utilizes the Systematic Literature Review (SLR) method of 13 scientific articles published in the ETRI Journal's 2024 special issue titled “Next-Gen AI and Quantum Technology.” The literature was selected based on inclusion criteria that included relevance to quantum AI, presence of experimental data, and contribution to computational efficiency.Findings: The study results show that approaches such as Quantum Reinforcement Learning and Quantum Kernel Classifiers can improve training efficiency and classification accuracy. Spiking Neural Networks technology reduced power consumption in AI-SoC and edge device designs. At the same time, the Quantum Key Distribution system demonstrated an error rate as low as 0.62% with WDM filter integration. The AONet video anomaly detection model achieves up to 97% AUC with the combination of a residual autoencoder and an attention module architecture.Implications & Recommendations: These findings indicate that quantum AI has great potential to overcome the limitations of classical computing in real-time applications and large-scale systems. However, challenges related to quantum noise, hardware stability, and integration with classical systems still need to be addressed. This research recommends strengthening hybrid infrastructure, developing interoperability standards, and utilizing multi-core architectures to support processing efficiency and data security.Contribution & Value Added: This study significantly contributes by systematically mapping existing methodologies and experiments in quantum AI, establishing a conceptual framework for future research avenues, and incorporating quantum AI technologies into industry, edge computing, and upcoming security systems
Mitigating Bias in AI: A Review of Sources, Impacts, and Strategies Miftah Maulana
Breakthroughs Information Technology Vol 1 No 1 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(1)-02

Abstract

Objective: This research examines trends, approaches, and application contexts of bias mitigation strategies in artificial intelligence (AI) systems. The primary focus is on how biases emerge in different sectors and how mitigation practices are developed to address equity and ethical challenges in AI development. Research Design & Methods: This research uses a Systematic Literature Review (SLR) approach with source selection and literature analysis from trusted databases such as IEEE Xplore, Scopus, SpringerLink, and ACM Digital Library. This study reviewed literature between 2018 and 2024 to ensure the relevance and novelty of findings in the context of bias mitigation in AI systems.Findings: The study results show that bias mitigation strategies have evolved from a narrow technical approach to a comprehensive system lifecycle-based approach. Notable innovations include the application of data-centric AI, fairness-aware algorithms, targeted data augmentation techniques, post-processing, bias auditing, and explainable AI. These approaches have been applied in various sectors. Implications & Recommendations: Effective bias mitigation demands a shift from a technical focus to a collaborative and multidisciplinary approach. System developers must embed fairness principles from the design stage, while regulators should promote transparency and accountability through strong policies. Systematic evaluation, cross-disciplinary collaboration, and public engagement are key for AI systems to be accepted as fair and responsible. Contribution & Value Added: This research provides a structured synthesis of bias mitigation approaches and demonstrates how they can be applied in real-world contexts. By offering practical guidance towards adaptive and integrated mitigation practices, this study contributes to strengthening ethical AI discourse
Adaptive it Investment for Smarter Governance: A Framework for Ai-Based Decision Support Systems in Public Sector Shifat Islam; Azanizawati Ma'aram
Breakthroughs Information Technology Vol 1 No 1 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(1)-05

Abstract

Objective: This research aims to explore the development of an adaptive Information Technology (IT) investment framework for artificial intelligence-based decision support systems (AI-DSS) in government, with a focus on improving strategic decision-making, transparency, and inclusive public services in the Indonesian public sector. Research Design & Methods: The research used a systematic literature review approach that refers to the PRISMA protocol. It analyzed 14 Scopus-indexed articles published between 2015 and 2024. Bibliometric analysis was conducted with the help of VOSviewer software to identify keyword trends and research clusters relevant to AI, decision making, and governance. Findings: AI-DSS contributes to strengthening decision-making in the public sector through real-time data processing, predictive analytics, and more agile responses to policy dynamics. However, challenges remain, such as the digital divide, regulatory limitations, a lack of technical competencies, and integration difficulties with legacy systems. Implications & Recommendations: The government is advised to implement a phased investment strategy supported by agile governance principles, strengthen the legal framework, and integrate inclusive technologies such as Big Data and Blockchain.Contribution & Value Added: This research provides a conceptual and practical framework for adaptive IT investment in AI-DSS, bridging technology and governance.
AI for Sustainability: Bibliometric Review of Power-Saving Algorithms in IoT and Edge Systems Aldo Hermaya Aditiya Nur Karsa
Breakthroughs Information Technology Vol 1 No 1 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(1)-04

Abstract

Objective: This research aims to explore the global trends, challenges, and future opportunities in the development of energy-efficient AI technology in the context of IoT, edge computing, and its potential to synergize with quantum computing. Research Design & Methods: This study uses a bibliometric-based systematic literature review approach to 113 documents from the Scopus database published in the 2021-2025 period. The analysis used VOSviewer to map keyword co-occurrence, collaboration between countries, topic trends, and citations. Findings: The research focus is expanding from basic topics of power efficiency to integrating AI with edge systems, adaptive communication protocols (such as BLE and LoRaWAN), and ML-based predictive models for smart home and agriculture. Countries like China, the United States, and India are central to global collaboration. The research also revealed the lack of studies on integrating quantum-inspired optimization in energy-efficient AI edge architectures. Implications & Recommendations: The findings indicate that further research directions are needed in the development of adaptive, efficient, and environmentally friendly AI architectures and that strengthening collaboration between countries is important. This research also supports strategies for developing low-emission and power-efficient digital technologies to support the global sustainability agenda. Contribution & Value Added: This research provides an important contribution in the form of current literature mapping and a scientific synthesis framework to formulate a research agenda and policy for sustainable AI technology in IoT and edge computing systems.
A Systematic Literature Review on The Role of Explainable AI in Enhancing Algorithmic Fairness Across Application Domains Wang Ying
Breakthroughs Information Technology Vol 1 No 1 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(1)-03

Abstract

Objective: Explainable Artificial Intelligence (XAI) is a field dedicated to improving the understanding and clarification of Machine Learning (ML) algorithms and their results. This research aims to determine commonly used XAI techniques, examine their classification, and assess their impact on the decision-making process. Research Design & Methods: This research utilizes the PRISMA framework to conduct a systematic literature review of 310 Scopus-indexed articles on Explainable Artificial Intelligence (XAI) from 2018 to 2024, using targeted keyword searches to ensure rigorous selection and transparency in the research process. Findings: The findings suggest that SHAP and LIME are the most frequently used explainable artificial intelligence (XAI) methods in the financial sector, due to their adaptability, clarity, and compatibility with various predictive models. However, there is still no standardized taxonomy, and only a few studies have focused on fairness or algorithmic fairness as a primary goal.Implications & Recommendations: This analysis highlights the need for a more comprehensive framework that combines explanation with fairness metrics. Future research should investigate the integration of Explainable Artificial Intelligence (XAI) within regulatory structures, such as the General Data Protection Regulation (GDPR), to ensure that it meets the needs of technical and ethical assessment.Contribution & Value Added: This research formulates a concise and applicable classification of XAI methods in the financial sector and highlights its relevance to equity issues to encourage the development of transparent, ethical, and auditable AI systems
Enhancing Zero-Shot Reasoning in Language Models Via Hybrid Instruction Marginalization Shirmohammad Tavangari; Aref Yelği
Breakthroughs Information Technology Vol 1 No 2 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(2)-01

Abstract

Objective: The study aims to enhance the reasoning abilities of Large Language Models (LLMs), which often remain shallow, inconsistent, and error-prone in complex multi-step tasks. It introduces the Hybrid Instruction Tuning Framework (HITF) to improve zero-shot reasoning through a task-aware hybrid selector that integrates both human-annotated and automatically generated examples. Research Design & Methods: HITF strengthens reasoning performance using three main techniques: synthesizing transitional results, context-aware prompt merging, and recurrent optimization, all executed without model recalibration. The framework is empirically evaluated using rigorous cognitive benchmarks, including SuperGLUE, MMLU, GSM8K, and FermiQA. Component isolation tests examine the independent contribution of the example selector, output synthesizer, and instruction combiner. Statistical variability assessments further validate result reliability. Findings: Results show that HITF consistently outperforms state-of-the-art methods across multiple metrics, demonstrating higher measurement accuracy, stronger argumentative quality, and deeper analytical processing. All core modules exhibit significant and measurable contributions, supported by stable statistical outcomes. Implications & Recommendations: Findings suggest that combining context-driven instruction selection with statistical consolidation techniques can substantially improve deductive reasoning in LLMs, particularly in data-scarce and example-free settings. Future research should explore HITF’s integration with larger models and its application in real-world reasoning-intensive domains. Contribution & Value Added: This study offers an innovative framework that enhances zero-shot reasoning without retraining. By merging hybrid instruction selection and iterative optimization strategies, HITF narrows the reasoning gap between LLMs and humans and provides a scalable, reliable approach for advancing high-level reasoning in modern language models
The Role of E-CRM in Shaping Customer Experience Satisfaction and Loyalty in The Banking Industry Pradeep Mamgain; Nibras Kadhim Abed-Ouj
Breakthroughs Information Technology Vol 1 No 2 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(2)-02

Abstract

Objective: This study aims to systematically synthesize the relationship between Electronic Customer Relationship Management (E-CRM), customer experience, customer satisfaction, and customer loyalty in the banking industry. This study addresses the theoretical gap regarding the mediating role of experience and satisfaction in the context of digital banking, as well as identifying areas for further empirical exploration.Research Design & Methods: This study uses a systematic literature review design by analysing national and international journal articles published between 2020 and 2025 on E-CRM in the banking sector. This analysis combines descriptive classification and synthesis to identify key variables, methodological trends, and research gaps related to the relationship between E-CRM, customer experience, satisfaction, and loyalty. Findings: This review shows a consistent positive and significant relationship between E-CRM and customer loyalty, which is largely mediated by customer experience and customer satisfaction across various sectors, indicating that improvements in digital service quality, ease of use, and personalization contribute to a stronger experience and satisfaction, which in turn strengthens loyalty. Additionally, contextual factors such as system quality, security, trust, and relationship quality significantly influence the effectiveness of E-CRM implementation.Implications & Recommendations: These findings theoretically confirm that E-CRM plays a central role in shaping customer experience, satisfaction, and loyalty, and practically recommend that banks develop user-oriented E-CRM through omnichannel integration, improved security, personalization, service quality, and a sustainable feedback system. Contribution & Value Added: This study contributes academically through the integrative framework of E-CRM–experience–satisfaction–loyalty while offering practical value by formulating strategic directions for E-CRM implementation and an agenda for further research.
Determinants of E-Learning and Blended Learning Effectiveness: A Systematic Review of Student Outcomes and Engagement Mohammad Sultan Ahmad Ansari
Breakthroughs Information Technology Vol 1 No 2 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(2)-03

Abstract

Objective: This study aims to identify and synthesize key factors that influence the effectiveness of e-learning and blended learning, as well as their impact on learning outcomes, student satisfaction, and engagement through a systematic and comprehensive analysis of global literature from 2015 to 2025. Research Design & Methods: A Systematic Literature Review (SLR) guided by PRISMA procedures was conducted using Scopus, ScienceDirect, ERIC, and Web of Science. Boolean search strategies and eligibility screening yielded 150 relevant peer-reviewed articles. Bibliometric mapping using VOSviewer and thematic analysis were used to classify the findings into five determining clusters: technological, pedagogical, individual, social, and institutional.Findings: This review shows that the effectiveness of learning in a digital environment arises from the interaction between robust technological infrastructure, high-quality instructional design, learner readiness, social presence, and institutional support. Engagement and satisfaction serve as mediating variables that connect these determinants with learning outcomes. Trends indicate a shift in global research from emergency online learning to sustainable, quality-oriented digital education.Implications & Recommendations: Institutions need to strengthen infrastructure and training, educators need to increase interaction and collaboration, while future research should focus on AI personalization and learning analytics to strengthen evidence of the effectiveness of digital learning.Contribution & Value Added: This study presents an integrated conceptual model that synthesizes previous findings and provides a holistic understanding of the factors that shape the effectiveness of e-learning and blended learning, while strengthening the global literature and supporting evidence-based decision-making in digital education transformation.
Zero-Trust Security Concept and Its Implementation in Cloud-Edge Environment Sreelatha R
Breakthroughs Information Technology Vol 1 No 2 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(2)-04

Abstract

Objective: This study aims to explore high-tech companies' understanding and perceptions of the Zero-Trust Security (ZTS) concept, identify the main challenges of its implementation in cloud–edge architectures, and analyze the security strategies used to effectively implement Zero-Trust in distributed environments. Research Design & Methods: This study uses a qualitative approach through the Systematic Literature Review (SLR) method on 25 scientific articles obtained from Scopus (18 articles), Google Scholar (7 articles), and additional sources through SciSpace. The analysis process was carried out through identification, screening, and thematic content analysis to map the concepts, challenges, and implementation strategies of Zero-Trust in cloud–edge.Findings: The results of the study show that Zero-Trust is understood as an identity-based security framework that emphasizes continuous verification, least privilege, and micro-segmentation. Key challenges include edge device heterogeneity, resource constraints, cross-platform policy orchestration, organizational readiness, and the inconsistency of distributed identity standards. Several effective strategies were identified, including adaptive authentication, identity-first architecture, AI-driven anomaly detection, blockchain integration, and policy-as-code for managing cloud–edge policies. Implications & Recommendations: Implementing Zero-Trust in a cloud-edge environment requires a phased approach that prioritizes identity management, automated policy orchestration, and security control integration tailored to the limitations of edge devices. Organizations are advised to strengthen their technical competencies, improve system interoperability, and adopt a telemetry-based security model. Contribution & Value Added: This research contributes to the latest conceptual synthesis regarding the implementation of Zero-Trust in cloud-edge architecture and fills the research gap related to the challenges and strategies of its application. The analytical framework can be used by practitioners, researchers, and policymakers in designing adaptive and sustainable Zero-Trust architectures.
Pedagogical and Ethical Dimensions of AI-Driven Learning Management Systems in the Generative AI Era: A Conceptual Review Shajeni Justin
Breakthroughs Information Technology Vol 1 No 2 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(2)-05

Abstract

Objective: This study examines the evolution of AI-driven Learning Management Systems (LMS), particularly in the era of Generative AI, by analyzing their pedagogical implications, academic integrity concerns, ethical challenges, and the tension between technological optimization and human-centered educational values. Research Design & Methods: This paper employs a conceptual and theoretical literature review of reputable publications addressing AI integration in higher education platforms. The selected studies are analyzed thematically to identify recurring patterns, critical debates, and emerging pedagogical and ethical issues. Findings: The review indicates that AI-enhanced learning platforms offer significant opportunities for personalization, adaptive feedback, and learning efficiency. However, they also introduce risks related to academic integrity, algorithmic bias, data privacy, and the erosion of cognitive autonomy. Trust and fairness depend on the alignment between system design, human-centered pedagogy, and institutional ethical governance. Implications & Recommendations: Higher education institutions should adopt transparent and pedagogically grounded AI policies that prioritize human-in-the-loop approaches, data protection, and responsible AI literacy. Strategic governance is essential to ensure that technological advancement supports, rather than replaces, core educational values. Contribution & Value Added: This conceptual review proposes an integrative framework linking pedagogy, ethics, and academic integrity, emphasizing that sustainable trust in AI-driven educational systems is shaped by value alignment rather than technological sophistication alone.

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