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All Journal ADI Journal on Recent Innovation (AJRI) International Transactions on Artificial Intelligence (ITALIC)
Ikhsan, Ramiro Santiago
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Industrial & Manufacturing Engineering Library & Information Science

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Integrating MidJourney Scripts into Architectural Design for Aesthetic Innovation Aini, Qurotul; Setiyowati, Harlis; Ikhsan, Ramiro Santiago; Amroni, Amroni; Pasha, Lukita
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.955

Abstract

The use of Artificial Intelligence (AI) in creative disciplines, particularly architecture, has introduced a paradigm shift in how design concepts are developed and visualized. This research explores the integration of MidJourney generative scripts within the architectural design process. Using a hybrid methodology of qualitative observation and computational experimentation, this study evaluates how AI-driven image generation influences form exploration, material perception, and aesthetic decision-making. The main objective is to identify how AI-based generative systems, specifically MidJourney, can enhance conceptual creativity and accelerate the design iteration cycle in architectural practice. Unlike previous procedural models limited to parametric control, this study introduces an adaptive AI human feedback mechanism enabling continuous co-evolution between designer intent and machine generation. Our findings indicate that AI-assisted workflows enhance the production of innovative architectural compositions, offering greater visual diversity, while simultaneously enhancing creative efficiency and reducing design fatigue. Quantitatively, AI integration improved design iteration speed by 65% and increased aesthetic consistency scores from 78% to 91%, compared to traditional workflows. Integrating MidJourney generative scripting into architectural workflows creates a dynamic feedback loop between human intuition and machine creativity, leading to a new model of aesthetic co-creation that expands the boundaries of contemporary architectural design.
Integrating AI-Driven Predictive Analytics and Smart Contracts for Data-Driven Supply Chain Risk Management Pujiati, Tri; Kamil, Mustofa; Silawati, Nur; Ikhsan, Ramiro Santiago
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.v7i1.1318

Abstract

Global supply chains face increasing uncertainty, while traditional risk management often lacks adaptability. This study investigates how AI driven predictive analytics and smart contracts enhance resilience, using mixed methods with case studies and big data analysis. A mixed method approach was employed, combining big data analytics from supply chain networks with machine learning models for predictive forecasting, supported by case studies from multinational manufacturing and logistics companies as well as secondary data from industry reports. The findings reveal that AI driven predictive models significantly improve demand forecasting accuracy, identify potential disruptions earlier, and enhance supplier risk assessment compared to conventional approaches, while integrating data from IoT enabled devices provides real time visibility across logistics operations. Overall, AI powered predictive analytics demonstrates substantial potential in transforming risk management within global supply chains by enabling proactive strategies and resilience, allowing organizations to reduce vulnerabilities, optimize performance, and strengthen competitiveness in dynamic markets, with future research suggested to explore the integration of blockchain for transparency and ethical governance in supply chain ecosystems.
Co-Authors Amroni, Amroni Harlis Setiyowati Mustofa Kamil, Mustofa Pasha, Lukita Pujiati, Tri Qurotul Aini Silawati, Nur