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All Journal Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) ADI Journal on Recent Innovation (AJRI)
Anjani, Sheila Aulia
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Unveiling New Horizons: AI-Driven Decision Support Systems in HRM - A Novel Bibliometric Perspective Shantilawati, Irma; Suri, Oryza Intan; Sunarjo, Richard Andre; Anjani, Sheila Aulia; Robert, Dariari
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 1 (2025): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i1.561

Abstract

The integration of Artificial Intelligence (AI)-driven Decision Support Systems (DSS) in Human Resources Management (HRM) has become crucial for optimizing workforce management and enhancing decision-making processes. This bibliometric analysis investigates the research landscape of AI-driven DSS in HRM from 2015 to 2024, using data from the Dimensions database and analyzed through VOSviewer. Key trends, influential authors, and significant publications are identified, revealing the dominant roles of the United States, China, and India, with institutions like MIT, Stanford University, and IIT Delhi leading in productivity and impact. Notable contributors such as Dwivedi, Lowry, and Bose are highlighted for their practical and theoretical advancements in the field. Influential journals including "Decision Support Systems", "Information & Management", and "Sustainability" are identified as shaping the research landscape. The findings emphasize the transformative impact of AI-driven DSS on HRM practices, offering insights into future research opportunities and applications. This study provides a comprehensive framework for understanding the current state and future directions of AI-driven DSS in HRM, contributing to both academic and practical advancements.
Integration of Business Intelligence and Predictive Analytics for Student Success Based on Blockchain: Integrasi Business Intelligence dan Analitik Prediktif untuk Keberhasilan Mahasiswa Berbasis Blockchain Rahardja, Untung; Rakhmansyah, Mohamad; Wijaya, Surta; Anjani, Sheila Aulia; Davies, Mary
Technomedia Journal Vol 10 No 1 (2025): June
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v10i1.2389

Abstract

In the digital era, education is undergoing a significant transformation, with predictive analytics becoming an important approach to increasing student success. Rapid advancements in technology have enabled institutions to collect and analyze diverse datasets, yet challenges remain in ensuring data accuracy, transparency, and reliability. This research explores the integration of blockchain technology to address data integrity challenges, with a focus on its application in predictive analytics. The objective is to enhance the reliability of student-related data while improving the effectiveness of academic performance predictions. Specifically, this research examines the relationship between Academic Performance Metrics (APM), Student Engagement Data (SED), Socioeconomic Factors (SEF), Blockchain-Enabled Data Integrity (BDI), and Predictive Algorithm Efficiency (PAE). Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, data were collected through structured surveys and institutional records involving higher education students. The constructs were validated through measurement model testing before proceeding to structural path analysis. The results show the significant influence of socio-economic factors and blockchain-based data integrity on academic outcomes, while student engagement and predictive algorithm efficiency also demonstrate moderate effects. The study also identifies areas that require improvement in predictive models, particularly regarding the alignment of input variables with algorithm design. These findings emphasize the importance of leveraging technology to develop more equitable and effective educational strategies, while underscoring the need for continued improvements in construct design to increase the reliability and validity of models. This research contributes to the growing field of educational data science by offering a blockchain-enhanced framework for predictive analytics in education.
Enhancing Trust and Efficiency in E-Commerce Transactions through Blockchain AI Synergy Rahardja, Untung; Daeli, Marda Leni; Anjani, Sheila Aulia; Pasha, Lukita; Asri, Asri; Zainarthu, Henry
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1317

Abstract

Co-Authors Asri asri Daeli, Marda Leni Davies, Mary Irma Shantilawati Pasha, Lukita Rakhmansyah, Mohamad Robert, Dariari Sunarjo, Richard Andre Suri, Oryza Intan Untung Rahardja Wijaya, Surta Zainarthu, Henry