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Facing Global Dynamics with Effective Strategy: A Tasted Organizational Change Management Approach Ronal Aprianto; Chandra Lukita; Asep Sutarman; Richard Andre Sunarjo; Rifqa Nabila Muti; Ellen Dolan
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 5 No. 1 (2025): April
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v5i1.177

In an era of rapid globalization and digital transformation, organizations must adopt adaptive strategies to navigate complex market shifts. This study explores a tested Organizational Change Management (OCM) approach to enhance business resilience and sustainability. Using a quantitative research methodology, we conducted a structured survey involving 200 business executives across various industries to analyze the effectiveness of strategic OCM models. The results indicate that organizations implementing structured OCM frameworks experience a 35% increase in operational efficiency, a 28% improvement in employee adaptability, and a 40% reduction in resistance to change. Our study introduces a novel hybrid model, integrating Kotters Change Model with Agile methodologies, ensuring a balance between structured change and adaptive flexlibility. The findings contribute to existing literature by demonstrating how real time data driven decision making enhances the success rate of organizational transformations. This research provides valuable insights for business leaders and policymakers in aligning corporate strategies with global market dynamics, fostering innovation, and maintaining competitive advantages.

Facing Global Dynamics with Effective Strategy: A Tasted Organizational Change Management Approach Ronal Aprianto; Chandra Lukita; Asep Sutarman; Richard Andre Sunarjo; Rifqa Nabila Muti; Ellen Dolan
International Journal of Cyber and IT Service Management Vol. 5 No. 1 (2025): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v5i1.177

In an era of rapid globalization and digital transformation, organizations must adopt adaptive strategies to navigate complex market shifts. This study explores a tested Organizational Change Management (OCM) approach to enhance business resilience and sustainability. Using a quantitative research methodology, we conducted a structured survey involving 200 business executives across various industries to analyze the effectiveness of strategic OCM models. The results indicate that organizations implementing structured OCM frameworks experience a 35% increase in operational efficiency, a 28% improvement in employee adaptability, and a 40% reduction in resistance to change. Our study introduces a novel hybrid model, integrating Kotters Change Model with Agile methodologies, ensuring a balance between structured change and adaptive flexlibility. The findings contribute to existing literature by demonstrating how real time data driven decision making enhances the success rate of organizational transformations. This research provides valuable insights for business leaders and policymakers in aligning corporate strategies with global market dynamics, fostering innovation, and maintaining competitive advantages.

Artificial Intelligence for Inclusive Learning within Sharia Educational Ethics Ninda Lutfiani; Kanon Mommsen Wongkar; Tatik Mariyanti; Rifqa Nabila Muti; Noah Rangi
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 2 (2026): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i2.930

The integration of Artificial Intelligence (AI) in education has created new opportunities to enhance inclusive learning across diverse educational contexts. In Islamic educational institutions, the adoption of AI presents a dual responsibility to embrace technological innovation while ensuring alignment with Sharia based ethical principles that emphasize justice, accountability, and respect for human dignity. This study investigates how AI is utilized to support inclusive learning within Islamic education settings in Southeast Asia using a qualitative descriptive approach with a multi-site case study design. Data were collected through semi-structured interviews with teachers, school leaders, and IT developers from selected Islamic educational institutions and analyzed using thematic coding techniques. The findings reveal that AI contributes positively to inclusive learning by enabling adaptive instruction, personalizing learning content, facilitating early identification of learning barriers, and supporting data-driven decision-making. When implemented within Sharia-compliant ethical frameworks, AI enhances fairness, balanced participation, and student engagement. However, the study also identifies several challenges, including limited content filtering mechanisms, insufficient data privacy governance, and the risk of algorithmic bias that may unintentionally marginalize certain student groups. This research highlights the importance of ethical governance and value-based AI design to ensure that AI adoption in Islamic education remains equitable, inclusive, and contextually appropriate. The findings provide strategic implications for institutional policy development, curriculum planning, and the long-term governance of AI-driven educational systems within Islamic educational environments.

Evaluating Machine Learning Techniques for Performance Monitoring and Continuous Improvement in Learning Factory Education Elika Setiawaty; Rifqa Nabila Muti; Kristina Vaher; Zulfadli Ardiansyah; Marta Rodriguez
International Transactions on Education Technology (ITEE) Vol. 4 No. 1 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i1.959

The rapid advancement of data-driven technologies has transformed the landscape of educational innovation, particularly within learning factory environments that simulate real industrial settings for experiential learning. This study aims to evaluate the effectiveness of various machine learning techniques in monitoring student performance and facilitating continuous improvement in the learning process. Using a quantitative approach, data were collected from student activities, production logs, and performance metrics within a university-based learning factory over one academic term. Several machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine, were applied to predict student performance levels and identify critical factors influencing learning efficiency. The analysis revealed that ensemble based models, especially Random Forest, achieved the highest prediction accuracy and provided valuable insights into performance trends, enabling proactive instructional interventions. Additionally, the integration of predictive analytics contributed to improved feedback mechanisms and optimization of task allocation, fostering both individual and group learning outcomes. The findings highlight the significant potential of machine learning in enhancing performance monitoring systems and promoting data informed decision-making in educational manufacturing contexts. This study concludes that the strategic adoption of machine learning techniques can substantially strengthen the feedback loop between learners and instructors, leading to more adaptive, efficient, and sustainable learning processes within the learning factory framework.

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