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Leveraging Big Data Analytics for Talent Management and Prediction in Human Resources Hariri, Ahmad; Prasetio, Rachmat; Al-Shammari, Abdullah; Kara, Sevda
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1780

Background. The increasing complexity of workforce management in modern organizations has driven the adoption of innovative tools such as Big Data Analytics (BDA) in human resources (HR). Talent management, encompassing recruitment, retention, and performance evaluation, has become a critical focus for organizations aiming to maintain competitiveness. Big Data Analytics enables HR professionals to identify patterns, predict trends, and make data-driven decisions, enhancing talent management processes. Despite its potential, the application of BDA in HR faces challenges, including data integration, privacy concerns, and skill gaps. Purpose. This study explores the role of Big Data Analytics in improving talent management and prediction, focusing on its impact on decision-making and organizational outcomes. Method. A mixed-method research design was employed, incorporating quantitative analysis of HR metrics and qualitative insights from interviews with HR professionals. Data were collected from 15 organizations across diverse industries, analyzing employee performance, recruitment patterns, and turnover rates. Predictive models were developed using machine learning algorithms to forecast talent trends and inform HR strategies. Results. The findings revealed that BDA significantly improved talent acquisition and retention processes, with a 25% increase in recruitment efficiency and a 30% reduction in turnover rates. Predictive models accurately identified high-potential candidates and flagged at-risk employees, enabling proactive interventions. Challenges related to data privacy and technical expertise were highlighted as areas for improvement. Conclusion. The study concludes that leveraging Big Data Analytics transforms talent management by enabling evidence-based decision-making and predictive insights. Addressing implementation challenges and investing in skill development will maximize its potential in HR practices.

Leveraging Big Data Analytics for Talent Management and Prediction in Human Resources Hariri, Ahmad; Prasetio, Rachmat; Al-Shammari, Abdullah; Kara, Sevda
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1780

Background. The increasing complexity of workforce management in modern organizations has driven the adoption of innovative tools such as Big Data Analytics (BDA) in human resources (HR). Talent management, encompassing recruitment, retention, and performance evaluation, has become a critical focus for organizations aiming to maintain competitiveness. Big Data Analytics enables HR professionals to identify patterns, predict trends, and make data-driven decisions, enhancing talent management processes. Despite its potential, the application of BDA in HR faces challenges, including data integration, privacy concerns, and skill gaps. Purpose. This study explores the role of Big Data Analytics in improving talent management and prediction, focusing on its impact on decision-making and organizational outcomes. Method. A mixed-method research design was employed, incorporating quantitative analysis of HR metrics and qualitative insights from interviews with HR professionals. Data were collected from 15 organizations across diverse industries, analyzing employee performance, recruitment patterns, and turnover rates. Predictive models were developed using machine learning algorithms to forecast talent trends and inform HR strategies. Results. The findings revealed that BDA significantly improved talent acquisition and retention processes, with a 25% increase in recruitment efficiency and a 30% reduction in turnover rates. Predictive models accurately identified high-potential candidates and flagged at-risk employees, enabling proactive interventions. Challenges related to data privacy and technical expertise were highlighted as areas for improvement. Conclusion. The study concludes that leveraging Big Data Analytics transforms talent management by enabling evidence-based decision-making and predictive insights. Addressing implementation challenges and investing in skill development will maximize its potential in HR practices.

Blockchain for Social Trust: Rebuilding Transparency in Public Sector Transactions through DLT Astawa, I Putu; Prasetio, Rachmat; Lim, Sofia
Journal of Social Science Utilizing Technology Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i2.2290

Background. The erosion of public trust in government institutions has become a critical global concern, driven largely by persistent issues of corruption, inefficiency, and opaque administrative processes. Amid this trust deficit, blockchain technology—especially Distributed Ledger Technology (DLT)—has emerged as a promising tool to rebuild transparency, accountability, and citizen engagement in the public sector. Purpose. This study aims to examine how blockchain can be strategically implemented to restore social trust by enhancing transparency in public sector transactions. Method. This study uses a qualitative method supported by several case studies, this study analyzes the initiative of real world blockchain adoption in countries such as Estonia, the United Arab Emirates, and Indonesia. Data was collected through analysis of policy documents, expert interviews, and comparative evaluation of the DLT -based public administration framework. Results. The findings indicate that blockchain’s immutable and decentralized architecture significantly mitigates information asymmetry, reduces opportunities for fraud, and enables real-time auditing of government activities. Moreover, smart contract integration allows for automatic enforcement of public service agreements, further reinforcing institutional integrity. However, the study also highlights critical challenges such as legal uncertainties, technological literacy gaps, and resistance to institutional change that may hinder effective implementation. Conclusion. In conclusion, while blockchain is not a panacea for all governance issues, it presents a powerful foundation for restoring social trust when embedded within a broader ecosystem of legal reform, digital literacy, and civic participation. This research contributes to the growing discourse on digital governance by offering a conceptual and empirical basis for blockchain-enabled transparency in the public sector.

Blockchain for Social Trust: Rebuilding Transparency in Public Sector Transactions through DLT Astawa, I Putu; Prasetio, Rachmat; Lim, Sofia
Journal of Social Science Utilizing Technology Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i2.2290

Background. The erosion of public trust in government institutions has become a critical global concern, driven largely by persistent issues of corruption, inefficiency, and opaque administrative processes. Amid this trust deficit, blockchain technology—especially Distributed Ledger Technology (DLT)—has emerged as a promising tool to rebuild transparency, accountability, and citizen engagement in the public sector. Purpose. This study aims to examine how blockchain can be strategically implemented to restore social trust by enhancing transparency in public sector transactions. Method. This study uses a qualitative method supported by several case studies, this study analyzes the initiative of real world blockchain adoption in countries such as Estonia, the United Arab Emirates, and Indonesia. Data was collected through analysis of policy documents, expert interviews, and comparative evaluation of the DLT -based public administration framework. Results. The findings indicate that blockchain’s immutable and decentralized architecture significantly mitigates information asymmetry, reduces opportunities for fraud, and enables real-time auditing of government activities. Moreover, smart contract integration allows for automatic enforcement of public service agreements, further reinforcing institutional integrity. However, the study also highlights critical challenges such as legal uncertainties, technological literacy gaps, and resistance to institutional change that may hinder effective implementation. Conclusion. In conclusion, while blockchain is not a panacea for all governance issues, it presents a powerful foundation for restoring social trust when embedded within a broader ecosystem of legal reform, digital literacy, and civic participation. This research contributes to the growing discourse on digital governance by offering a conceptual and empirical basis for blockchain-enabled transparency in the public sector.

Utilization of Multi-Agent Systems in Managing Smart Transportation Systems in Urban Areas Hayati, Amelia; Prasetio, Rachmat; Puspitasari, Mariana Diah; Jiao, Deng
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1534

Urban areas face increasing challenges in managing transportation systems due to rising population densities and traffic congestion. Traditional traffic management methods often lack the flexibility and responsiveness needed to address dynamic conditions in real time. This study explores the utilization of multi-agent systems (MAS) as a solution for optimizing smart transportation systems within urban environments. The research aims to evaluate the effectiveness of MAS in improving traffic flow, reducing congestion, and enhancing system responsiveness through autonomous decision-making and coordination among multiple agents. A simulation-based methodology was employed to analyze MAS performance in managing various transportation variables, including traffic density, signal timing, and incident response. Each agent was programmed to perform specific tasks, such as monitoring traffic, optimizing traffic signals, and re-routing vehicles, with collaborative decision-making to address congestion in real time. Results indicate that MAS implementation led to a 30% improvement in traffic flow efficiency and a 25% reduction in congestion levels. The system also demonstrated adaptive capabilities, allowing for real-time adjustments to unexpected conditions, such as accidents or road closures. The findings suggest that multi-agent systems provide a viable, scalable solution for smart transportation management in complex urban settings. Implementing MAS can significantly enhance the efficiency and adaptability of urban transportation systems, contributing to more sustainable and efficient mobility solutions in rapidly growing cities.

Utilization of Big Data in Improving the Efficiency of E-Business Systems in Indonesia Nugroho, Agung Yuliyanto; Prasetio, Rachmat; Wong, Lucas; Rao, Ananya
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2251

The rapid growth of digital technology in Indonesia has fostered the expansion of e-business systems, which in turn has generated vast volumes of data. However, many e-business platforms still face challenges in utilizing this data effectively to improve operational efficiency and decision-making. This research was conducted to explore the utilization of big data in enhancing the efficiency of e-business systems in Indonesia. The main objective of the study is to analyze how the integration of big data analytics contributes to optimizing business processes, customer engagement, and overall system performance in the Indonesian digital commerce ecosystem. A mixed-method approach was employed, combining quantitative surveys of 120 e-business practitioners with qualitative interviews involving 15 data analysts and IT managers from various sectors such as retail, fintech, and logistics. Data were analyzed using statistical tools and thematic coding to derive patterns and insights. The findings indicate that e-businesses implementing big data strategies reported a significant improvement in system responsiveness, personalized customer services, and data-driven decision-making. Moreover, big data utilization has been linked to enhanced supply chain management and real-time monitoring capabilities. Despite these benefits, challenges such as data privacy concerns, lack of skilled personnel, and high infrastructure costs remain significant barriers. In conclusion, the study confirms that the effective use of big data plays a crucial role in improving the efficiency and competitiveness of e-business systems in Indonesia. Future initiatives should focus on strengthening data governance and investing in human capital to maximize big data’s potential.

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