Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Record and Library Journal ENDLESS : International Journal of Future Studies
Samsuddin, Samsul Farid
Unknown Affiliation
Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Knowledge mapping of research data management: Uncovering themes and developments through co-occurrence and bibliometric analysis Ismail, Mohd Ikhwan; Abrizah, A.; Samsuddin, Samsul Farid
Record and Library Journal Vol. 11 No. 1 (2025): June
Publisher : D3 Perpustakaan Fakultas Vokasi Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/rlj.V11-I1.2025.234-258

Abstract

Background of the study: Discussions on RDM have grown rapidly in scholarly platforms, emerging as a key topic within library and information science (LIS). While existing studies have reviewed and analyzed RDM literature, their scope is often limited to specific areas or timeframes. It is necessary for a detailed and current analysis of RDM literature, providing deeper insights into its complexities, evolution, and future directions. Purpose: The study presents mapping knowledge domains as a method to uncover the thematic landscape, identify significant clusters, and provide a structured understanding of interconnected concepts within the field of RDM. Method: Data were retrieved from Elsevier’s Scopus database as of August 2023. The study conducts bibliometric analysis to examine geographical distribution, publication outlet, authorship trends, and performance metrics within the field. Findings: The dataset spans from 1977 to 2023, with an increase in publications exceeding ten per year from 2012 onwards, amounting to 684 documents in various languages and reference types. The study identifies four research clusters derived from these documents, highlighting key themes namely, RDM services, data sharing, information systems, and data management. Conclusion: The findings underscore the growth of RDM-related research and contribute to a deeper understanding of the underlying structure of RDM, for researchers, practitioners, and policymakers, enabling them to address current challenges and anticipate future developments in this rapidly evolving field.
Artificial Intelligence in Archival Science: Enhancing Records Preservation, Retrieval Accuracy, and Knowledge Accessibility in the Digital Era Fatmawati, Endang; Shuhidan, Shamila Mohamed; Samsuddin, Samsul Farid; Husna, Jazimatul; Irawati, Yayuk Endang; Rafa, Minan Faiz Fausta
ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES Vol. 9 No. 2 (2026): ENDLESS: International Journal of Future Studies
Publisher : Global Writing Academica Researching & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/endlessjournal.v9i2.381

Abstract

This study examines the transformative role of artificial intelligence (AI) in archival science, with a particular focus on enhancing records preservation, retrieval accuracy, and knowledge accessibility in the digital era. The increasing volume and complexity of digital records have posed significant challenges for traditional archival practices, necessitating the integration of advanced technological solutions. In response, AI has emerged as a promising approach to automate archival processes and improve the efficiency of information management systems. This study adopts a literature review methodology by systematically analyzing relevant scholarly publications to synthesize current knowledge on AI applications in archival contexts. The review draws upon peer-reviewed articles indexed in reputable databases to ensure the credibility and relevance of the findings. The results indicate that AI technologies, including machine learning, natural language processing, and computer vision, significantly contribute to improving digital preservation strategies and minimizing data degradation risks. AI-driven retrieval systems enhance the precision and speed of information access through intelligent indexing and semantic search capabilities. The study also finds that AI facilitates broader knowledge accessibility by enabling user-centered interfaces and adaptive information systems. Despite these advancements, several challenges persist, including ethical concerns, data bias, and the need for standardized implementation frameworks. This study contributes to the growing body of knowledge by providing a comprehensive synthesis of AI applications in archival science and offering insights for future research and practical implementation.
Co-Authors A. Abrizah Endang Fatmawati Irawati, Yayuk Endang Ismail, Mohd Ikhwan Jazimatul Husna Rafa, Minan Faiz Fausta Shamila Mohamed Shuhidan, Shamila Mohamed