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All Journal JURNAL EDUCATION AND DEVELOPMENT Economics and Business Journal Multidisciplinary Indonesian Center Journal
Han, Yonghwa
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Other

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ANALISIS PENGARUH EMPLOYEE ENGAGEMENT, EMPLOYEE EMPOWERMENT, DAN CAREER DEVELOPMENT TERHADAP PRODUCTIVITY LEVEL DAN TURNOVER INTENTION PADA INDUSTRI TEKNOLOGI DI INDONESIA Wulandari, Aghnia; Han, Yonghwa; Waluyo, Tri
JURNAL EDUCATION AND DEVELOPMENT Vol 13 No 1 (2025): Vol 13 No 2 Mei 2025
Publisher : Institut Pendidikan Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37081/ed.v13i1.6790

Abstract

Penelitian ini menganalisis pengaruh employee engagement, employee empowerment, dan career development terhadap tingkat produktivitas dan intensitas turnover karyawan di industri teknologi di Indonesia. Melalui pendekatan kuantitatif dengan menggunakan data dari karyawan di beberapa perusahaan teknologi, penelitian ini menemukan bahwa ketiga variabel independen tersebut memiliki dampak positif yang signifikan terhadap produktivitas karyawan. Hasil penelitian ini memberikan wawasan bagi manajer sumber daya manusia untuk merancang strategi yang efektif dalam meningkatkan keterlibatan dan pengembangan karir karyawan, sehingga dapat meningkatkan produktivitas dan retensi dalam organisasi atau perusahaan.
Strategic Financial Management in The Digital Age: Leveraging Artificial Intelligence (AI) for Enhanced Creativity and Insight Han, Yonghwa; Nurwulandari, Andini; Hasanudin
Economics and Business Journal (ECBIS) Vol. 3 No. 5 (2025): July
Publisher : PT. Maju Malaqbi Makkarana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47353/ecbis.v3i5.226

Abstract

This study investigates the influence of artificial intelligence (AI) integration on strategic financial management in large corporations. Focusing on a sample of 20 Fortune 500 companies from diverse industries, the research employs a quantitative, descriptive-analytical approach utilizing secondary data from financial reports and AI system logs. The findings reveal that AI adoption significantly enhances forecasting accuracy, risk identification, and operational efficiency, while also enabling financial managers to redirect resources toward creative and strategic initiatives. However, the study also identifies challenges related to data quality, ethical considerations, and skill gaps in AI utilization. The results highlight the importance of a balanced approach that combines AI-driven insights with managerial intuition to maximize value creation in the digital age.
SUSTAINABLE AGILITY-DRIVEN CULTURE IN HUMAN RESOURCE MANAGEMENT: A PRIDE FRAMEWORK FOR FUTURE-READY ORGANIZATIONS Wulandari, Aghnia; Efendi, Suryono; Hasanudin; Han, Yonghwa
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 3 No. 1 (2026): Vol. 3 No. 1 Edisi Januari 2026
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v3i1.1571

Abstract

This research develops the PRIDE Framework to integrate five interdependent dimensions, People (People-Centric Excellence), Resilience, Innovation, Development, and Empowerment, into a unified model explaining how HRM practices drive both sustainability and agility. A systematic literature review guided by PRISMA principles was conducted, selecting peer-reviewed empirical and conceptual studies that examine HRM, organizational agility, and sustainability. Thematic synthesis and reflexive analysis produced a circular puzzle architecture representing these dimensions and their interconnections. Findings reveal that integrated people-centric policies, robust knowledge management, embedded innovation practices, continuous learning, and distributed decision-making create synergistic capabilities that enable rapid adaptation while maintaining long-term viability. Critical enablers include psychological safety, holistic wellness initiatives, adaptive learning programs, and outcome-based empowerment. The framework addresses gaps by showing sustainability and agility as complementary imperatives rather than competing priorities. Future research should validate the framework through multi-level and longitudinal studies, incorporate diverse language sources for broader context, and explore the impacts of AI-enabled HRM on each dimension. Limitations involve temporal and linguistic scope.
ARTIFICIAL INTELLIGENCE IN FINANCIAL RISK MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW ON ENHANCING ORGANIZATIONAL RESILIENCE FOR FUTURE GLOBAL FINANCIAL CRISES Han, Yonghwa; Nurwulandari, Andini; Hasanudin; Wulandari, Aghnia
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 3 No. 1 (2026): Vol. 3 No. 1 Edisi Januari 2026
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v3i1.1572

Abstract

This study explores how incorporating artificial intelligence improves institutional resilience and overcomes the rigidity of conventional, data-based methods to alter financial risk management. To find patterns in AI applications, resilience theory, and integration pathways, a qualitative systematic literature review was carried out utilizing theme synthesis in accordance with PRISMA peer-reviewed protocols. Findings show that AI techniques, machine learning for tail-risk detection, deep learning for high-frequency forecasting, and explainable AI for transparent decisions, yield up to 28% reductions in forecasting errors and halve recovery times during crises. The hybrid CNN Transformer architectures and transformer-based NLP models significantly enhance predictive accuracy and forward-looking insights. The study suggests financial institutions adopt integrated AI frameworks, invest in data quality and human–AI collaboration, and implement principle-based governance to balance innovation with fairness and stability. Limitations include reliance on published literature and limited representation of emerging AI models, warranting future longitudinal and context-specific empirical research.
Co-Authors Aghnia Wulandari ANDINI NURWULANDARI Efendi, Suryono Hasanudin Tri Waluyo