cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Harminto Mulyo
Contact Email
minto@generatedp.com
Phone
+6282226962023
Journal Mail Official
bitgeneratedp@gmail.com
Editorial Address
Jl. Bugel KM 2 Troso Village RT 6 RW 3 No. 6, Pecangaan District, Jepara Regency, Central Java Indonesia, 59462
Location
Kab. jepara,
Jawa tengah
INDONESIA
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 5 Documents
 1 
Search results for , issue "Vol 1 No 1 (2025)" : 5 Documents clear
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

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 1 No 2 (2025) Vol 1 No 1 (2025)