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Penelitian ini mengkaji transformasi strategis manajemen risiko finansial melalui penerapan Digital Twins (DT) dengan pendekatan Systematic Literature Review (SLR). DT berevolusi dari sekadar model manufaktur menjadi sistem cerdas yang mampu memprediksi perilaku entitas finansial secara real-time. Hasil kajian menunjukkan bahwa DT mendukung kerangka Prevention, Preparedness, Response, Recovery (PPRR), memperkuat resiliensi rantai pasok, serta meningkatkan efektivitas stress testing perbankan sesuai regulasi Basel III. Selain itu, DT berperan dalam deteksi penipuan, akuntansi karbon, dan simulasi kebijakan makroekonomi. Studi ini menegaskan bahwa DT mampu meningkatkan akurasi prediksi risiko hingga 95% dan mengurangi downtime operasional sebesar 20%. Namun, keterbatasan standar global, integrasi data lintas sistem, serta hambatan regulasi masih menjadi tantangan utama. Agenda riset masa depan diarahkan pada pengembangan interoperabilitas global, integrasi teknologi AI, serta evaluasi ROI jangka panjang untuk memperkuat ketahanan finansial berbasis DT.

This study investigates the adoption of adaptive DevOps practices in embedded systems used in safety-critical industrial applications. Traditional DevOps models, which are primarily designed for cloud-based systems, face significant challenges when applied to embedded platforms due to hardware constraints, real-time performance requirements, and stringent safety standards. The research focuses on developing a tailored DevOps framework that integrates continuous integration/continuous delivery (CI or CD) pipelines, automation, real-time monitoring, and safety assurance processes to enhance system reliability, performance, and compliance with regulatory standards. The study uses a case study methodology, involving embedded system teams across multiple industrial sectors, to assess the impact of these adapted DevOps practices on system stability and operational efficiency. Key findings show that the adoption of adaptive DevOps practices led to significant improvements in system reliability, performance, and deployment stability. Continuous feedback mechanisms allowed for early issue detection and faster resolution, leading to enhanced system uptime and responsiveness. Additionally, the integration of safety assurance into the DevOps pipeline ensured that safety-critical systems complied with required safety integrity levels and certification standards. The study further explores the integration of DevOps with embedded safety-critical systems, highlighting the benefits of cross-domain collaboration, enhanced communication, and the ability to address the unique challenges of these platforms. The research also underscores the limitations of conventional DevOps models in embedded systems and presents practical implications for the wider adoption of DevOps in safety-critical industrial applications. Future research is recommended to refine DevOps frameworks for embedded systems, integrating emerging technologies like the Industrial Internet of Things (IIoT) and Digital Twins to further optimize performance, security, and predictive maintenance.

Smart industry has become an important trend in the development of Industry 4.0, especially in promoting the creation of efficient systems in the manufacturing sector. Various countries and studies are encouraging the application of technologies such as IoT, digital twins, artificial intelligence, and smart factories to improve industrial efficiency and sustainability. Therefore, studies related to smart industry are important and necessary especially on the context of smart manufacturing in order to see the direction of future research trends. This study uses a qualitative approach with literature data from the Scopus database covering the period 2020 to 2025. Research trend analysis was conducted through data processing using Bibliometric analysis in R Studio and the VOSviewer applications. To identify the latest research trends regarding smart industry, particularly in the context of Industry 4.0 and smart manufacturing, this analysis can provide a comprehensive picture of future research developments and directions within a global context.

This study aims to conduct a systematic literature review and bibliometric analysis on Transformational Leadership (TL), based on empirical research. Despite extensive studies on TL in recent decades, comprehensive research remains limited. The methodology used is a Systematic Literature Review (SLR) of articles containing “Transformational Leadership” in the title, abstract, or keywords, sourced from the Scopus database, which yielded 1,297 publications from 1977 to 2025. The data were analyzed using VOSviewer software. The findings suggest that TL is an innovative model in vocational education, bridging industrial systems with academic environments for authentic, practice-based learning. However, challenges include limited resources, insufficient instructor training, and inadequate institutional support. Recent innovations, such as advanced ICT, holographic technology for real-time interaction, and digital twins for process optimization, have enhanced the model's effectiveness. The study also discusses the conceptual framework of TL. This research uses the Scopus database, and future studies could broaden its scope by incorporating other databases like Web of Science. Practically, the study’s findings suggest integrating modern pedagogical strategies, including gamification, simulation, and project-based learning, to enhance student engagement and understanding of manufacturing processes. Socially, it encourages educational institutions to invest in infrastructure, strengthen industry partnerships, and provide continuous instructor training. This study highlights the growing global research on TL and calls for deeper exploration of the topic in existing literature.

This paper investigates how Big Data Analytics (BDA) can accelerate the transition to a low-carbon digital economy. We present a systematic literature-based research framework (2015–2025) that synthesizes applications of BDA in energy systems, transportation, industry and supply chains. The methodology combines systematic review and conceptual modelling to identify pathways through which BDA reduces emissions: (1) demand-side optimization, (2) operational efficiency, (3) predictive maintenance and (4) data-driven policy and market instruments. Results highlight concrete case examples smart grids, digital twins, and green supply-chain analytics and quantify benefits reported in recent literature. Key challenges such as data governance, carbon costs of computing, and policy integration are discussed. The paper concludes with policy recommendations and a research agenda to align digitalization with decarbonization goals.

Artificial Intelligence (AI) is quickly becoming a game changer in the way businesses build and manage their strategies. This article explores how AI is helping organizations make faster and smarter decisions, streamline operations, and spark innovation across various industries. With the ability to process massive amounts of data, AI tools can uncover valuable insights about market trends and customer behavior, allowing companies to respond more accurately and stay ahead of the competition. From machine learning and generative AI to natural language processing and digital twins, these technologies are transforming everything from internal workflows to how businesses connect with customers. The article also offers a practical roadmap for adopting AI in a business setting, covering steps like evaluating readiness, running pilot projects, and measuring success through return on investment (ROI). It emphasizes the need for strong data infrastructure, skilled teams, and a culture that supports innovation and data-driven thinking. Challenges such as algorithmic bias, data privacy, and internal resistance to change are also addressed. Real-world examples from banking, retail, and manufacturing show how AI can deliver real impact improving efficiency, increasing customer satisfaction, and driving business growth. Ultimately, embracing AI isn’t just about keeping up with technology it’s about shaping the future of smart, strategic, and ethical business.

Aquaculture is a vital sector in global food production, providing essential protein sources. However, the industry faces significant challenges, including high energy consumption and environmental impact. The integration of renewable energy, particularly solar power, with automation and IoT systems offers a promising solution to enhance energy efficiency, sustainability, and productivity in aquaculture operations. This study aims to evaluate the effectiveness of solar powered autonomous systems in reducing energy usage, improving operational efficiency, and promoting environmental sustainability in aquaculture. Literature Review: Recent research has explored various technologies, such as Digital Twins (DTs) and Precision Fish Farming (PFF), which integrate IoT sensors for real time monitoring and optimization of fish farming operations. The combination of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, has further advanced the industry by enabling automated decision making and predictive analytics. Solar power integration with IoT systems has been shown to significantly reduce operational costs, minimize carbon emissions, and enhance the sustainability of aquaculture practices. These advancements have the potential to address the challenges of energy consumption and environmental degradation in the industry. Materials and Method: This research utilizes a hybrid solar powered IoT system for aquaculture, integrating solar panels, IoT sensors, and automated control systems. The system monitors key water quality parameters, such as pH, dissolved oxygen, turbidity, and temperature, to maintain optimal conditions for aquatic life. Data is collected through IoT sensors and analyzed through a cloud-based platform. A pilot study is conducted on a small scale aquaculture farm to evaluate the system's performance, including energy consumption, water quality management, and fish health. Energy savings, operational efficiency, and environmental impact are assessed. Results and Discussion: The integration of solar powered IoT systems significantly reduced energy consumption compared to traditional systems, with a notable decrease in grid electricity reliance. The system successfully maintained optimal water quality conditions, enhancing fish health and growth. Solar powered systems proved reliable, even in regions with variable sunlight, and demonstrated improvements in operational efficiency through automation. The environmental benefits were evident, with a reduction in carbon emissions and lower operational costs. The study highlights the feasibility of solar powered IoT systems as a sustainable solution for modern aquaculture operations.

Artificial Intelligence (AI) is transforming mechanical engineering and industrial processes by introducing unprecedented levels of efficiency, precision, and innovation. From predictive maintenance and autonomous robotics to material optimization and digital twins, AI-enabled systems are reshaping the industry landscape. This article examines key applications of AI in mechanical engineering, exploring how they contribute to sustainable industrial innovation, improve productivity, and pave the way for future advancements.