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Data : Journal of Information Systems and Management
Published by PT Penerbit Ilmiah Indonesia
ISSN : -     EISSN : 30310008     DOI : https://doi.org/10.61978/data
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Data : Journal of Information Systems and Management with ISSN Number 3031-0008 (Online) published by Indonesian Scientific Publication, is a leading open-access and peer-reviewed scientific journal dedicated to publishing high-quality research in the field of information systems and management. Since its establishment, Data has been committed to advancing knowledge and understanding of the integration between information systems and management in a global context. The journal publishes research articles, technical papers, theoretical studies, and case studies that undergo rigorous peer review to ensure the highest standards of academic integrity and originality.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 3 Documents
 1 
Search results for , issue "Vol. 3 No. 4 (2025): October 2025" : 3 Documents clear
A Narrative Review of the Integration of Big Data Analytics and Business Intelligence in Organizational Decision-Making Noviany , Henny
Data : Journal of Information Systems and Management Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/data.v3i4.710

Abstract

The integration of Big Data Analytics (BDA) and Business Intelligence (BI) has become increasingly vital for enhancing strategic decision-making within contemporary organizations. This narrative review aims to investigate how the convergence of BDA and BI influences decision-making processes, particularly in sectors such as finance, healthcare, manufacturing, and retail. The review employed comprehensive literature searches across Scopus, Web of Science, and Google Scholar using keyword combinations like “Big Data Analytics”, “Business Intelligence”, and “Decision Making”. Inclusion criteria prioritized peer-reviewed journal articles from the past decade. Findings reveal that BDA enables organizations to analyze large-scale data for hidden insights, while BI transforms these insights into visual and actionable intelligence. Together, they contribute to increased decision accuracy, cost reduction, and enhanced performance. Artificial Intelligence (AI), particularly machine learning and natural language processing, further amplifies these outcomes by enabling rapid and nuanced analysis of structured and unstructured data. However, systemic barriers persist, including fragmented data infrastructure, limited human capital, and concerns over data ethics and compliance. This review highlights the need for organizations to adopt a holistic, cross-functional approach to data integration while investing in digital skills development. It also underscores the importance of regional readiness and industry-specific strategies. The findings inform policymakers, practitioners, and scholars on the strategic imperatives for integrating BDA and BI to sustain innovation, responsiveness, and competitive advantage in the digital age
Operationalizing Responsible AI in Health Systems: Delphi Based Governance Metrics for Indonesia Puspitasari, Devi; Yuni T, Veronika
Data : Journal of Information Systems and Management Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/data.v3i4.910

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare delivery in Indonesia. However, the responsible governance of AI systems especially in clinical settings remains underdeveloped. This study aims to identify and prioritize measurable governance indicators for AI in Indonesian healthcare through a Delphi based expert consensus process. A three round modified Delphi method was employed, engaging 30 interdisciplinary experts from healthcare, IT, cybersecurity, ethics, law, and patient advocacy. The process began with 40 indicators drawn from global frameworks (WHO, EU AI Act, ISO/IEC 42001, NIST RMF) and national references (UU PDP, SATUSEHAT). Experts rated each indicator on a 1–9 Likert scale across two iterative rounds. Consensus was defined as median ≥7 and IQR ≤1.5 using RAND/UCLA criteria.Out of 40 indicators, 24 achieved consensus. High priority indicators included clinical safety metrics (e.g., AUROC), data privacy compliance (PDP Law documentation), system integration (SATUSEHAT compatibility), and cybersecurity readiness (incident response plans). Transparency related indicators (e.g., training data summaries, model cards) failed to reach consensus, suggesting institutional gaps in AI explainability. The Delphi process underscored the importance of participatory governance, stakeholder trust, and contextual adaptation of international standards. Consensus indicators reflect domains where operational familiarity and regulatory anchors already exist, while non consensus areas highlight the need for capacity building and clearer guidelines. This study delivers a validated, measurable governance framework to guide responsible AI adoption in Indonesian healthcare. It supports policymaking, institutional audits, and procurement strategies aligned with both local regulation and global standards. Future work should pilot these indicators and expand their use in health system assessments and continuous governance improvement.
Dynamic Capabilities in Digital Transformation: Unpacking the Mediated Effects of IS Integration on Managerial Agility and Decision Speed Ikbal, Muhammad
Data : Journal of Information Systems and Management Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/data.v3i4.926

Abstract

This study examines how information system (IS) integration affects managerial decision speed in Indonesian public and private sector organizations. Using survey data from 185 organizations and structural equation modeling, we test a mediation model involving system quality, organizational agility, and big data analytics capability (BDAC). Results show that IS integration directly accelerates decision speed and indirectly strengthens it through the three mediators. Private sector firms benefit more due to higher digital readiness and flexible structures, while environmental turbulence further shapes outcomes. The study contributes by extending IS success theory with dynamic capabilities and highlighting sector-specific strategies for digital transformation. Findings emphasize that successful digital transformation requires not only technical solutions but also cultural, structural, and strategic alignment.

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