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All Journal Rechtsnormen Journal of Law Journal of Computer Science Advancements Journal of Noesantara Islamic Studies Journal of Moeslim Research Technik Journal of Selvicoltura Asean Journal of Tecnologia Quantica Techno Agriculturae Studium of Research Research of Scientia Naturalis Journal of Social Entrepreneurship and Creative Technology
Fujita, Miku
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Physics Other

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DIGITAL CREATIVITY AND SOCIAL VALUE CREATION: ENTREPRENEURIAL STRATEGIES IN TECHNOLOGY-DRIVEN COMMUNITIES Mayndarto, Eko Cahyo; Suzuki, Ren; Fujita, Miku
Journal of Social Entrepreneurship and Creative Technology Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v3i2.3631

Abstract

The rise of digital technologies has revolutionized entrepreneurial strategies, enabling the integration of creativity into business models that address social challenges. Digital creativity is now a vital tool for fostering social value in technology-driven communities. This research explores how entrepreneurs leverage digital creativity to create social value, focusing on sectors such as health, sustainability, and cultural preservation. The study aims to investigate the relationship between digital creativity and social value creation within entrepreneurial strategies. A qualitative approach was employed, utilizing case studies, semi-structured interviews, and document analysis of 10 digital platforms from various technology-driven sectors. The findings reveal that digital creativity not only contributes to business success but also facilitates community engagement, empowerment, and the development of social initiatives. Platforms with a focus on transparency and active community participation showed higher levels of social value creation, particularly in health and sustainability sectors. The study concludes that digital creativity in entrepreneurial strategies is an effective driver of social change and can contribute to sustainable development. Furthermore, the research emphasizes the importance of balancing profit with social impact, offering a framework for integrating digital creativity into business practices for broader societal benefits.
ALGORITHMIC INTELLIGENCE IN ENGINEERING DESIGN: INTEGRATING MACHINE LEARNING WITH PHYSICAL MODELING Erwis, Fauzi; Fujita, Miku; Suarnatha, I Putu Dody; Wilson, Amanda
Journal of Moeslim Research Technik Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i2.3467

Abstract

Increasing complexity in engineering systems demands design methodologies that balance computational efficiency, predictive accuracy, and physical reliability. Traditional physics-based simulations ensure mechanistic consistency but are computationally expensive, while purely data-driven machine learning models offer speed yet often lack interpretability and physical compliance. Integrating algorithmic intelligence with physical modeling has therefore emerged as a promising paradigm in advanced engineering design. This study aims to develop and evaluate a hybrid framework that integrates machine learning algorithms with governing physical equations to enhance design performance, robustness, and computational efficiency. A mixed-methods computational design was employed using 15,000 high-fidelity simulation datasets across structural, aerodynamic, and thermal engineering cases. Three modeling configurations—physics-based models, data-driven models, and hybrid physics-informed machine learning models—were comparatively analyzed using performance metrics including mean squared error, R², runtime efficiency, robustness testing, and constraint violation indices. Statistical analyses were conducted to determine significance of performance differences. Hybrid models achieved superior balance, reaching R² = 0.97 with significantly reduced runtime compared to physics-based simulations (p < 0.001), while maintaining substantially lower physical constraint violations than purely data-driven models. Sensitivity and uncertainty analyses confirmed enhanced robustness under parameter perturbation. Algorithmic intelligence integrated with physical modeling represents an epistemologically coherent and practically effective approach, advancing engineering design toward trustworthy, efficient, and physically consistent computational frameworks.
Co-Authors Arnes Yuli Vandika Dadang Muhammad Hasyim Erwis, Fauzi I Putu Dody Suarnatha Judijanto, Loso Mayndarto, Eko Cahyo Nakamura, Yui Nampira, Ardi Azhar Nasution, Ingka Harsani Nishida, Daiki Sato, Haruka Setiawan Setiawan Suzuki, Ren Takahashi, Haruto Tanaka, Kaito WIJAYA WIJAYA, WIJAYA Wijaya, Hamid Wilson, Amanda