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All Journal IAIC Transactions on Sustainable Digital Innovation (ITSDI) International Transactions on Artificial Intelligence (ITALIC)
Konita Lutfiyah
IPB University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology

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Assessing the Environmental and Economic Impact of Smart Grid Integration in Renewable Energy Management Henry Henry; Konita Lutfiyah; Harry Agustian; Nicholas Lachlan
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 7 No 1 (2025): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v7i1.708

Abstract

The global transition to renewable energy aims to reduce environmental impacts and combat climate change, yet challenges arise due to the intermittent nature of renewable sources, complicating their integration into traditional power grids and requiring advanced management solutions. Smart grid technology presents promising capabilities to optimize renewable energy management, promoting both environmental sustainability and economic efficiency. This study evaluates the environmental and economic impacts of smart grid integration, fo- cusing on carbon emission reductions, enhanced energy efficiency, and cost savings for energy providers and consumers. Using Structural Equation Modeling via SmartPLS, data were collected and analyzed from various stakeholders engaged in renewable energy and smart grid applications, allowing a detailed assessment of the relationships between smart grid integration, environmental outcomes, and economic benefits. Results indicate that smart grid integration significantly reduces carbon emissions and improves energy efficiency by over 30% while economically, it yields substantial cost savings, cutting operational expenses by up to 25% over time. The SmartPLS analysis confirms a positive relationship between smart grid deployment and both environmental and economic outcomes, highlighting that smart grids not only support emission reductions but also deliver considerable financial benefits in renewable energy management. These findings offer important insights for policymakers and industry stakeholders, emphasizing the role of smart grids in advancing sustainable and economically viable global energy systems.
Vision-Based Pattern Recognition Models for Intelligent Human Robot Interaction in Smart Spaces Muhamad Faizal Fazri; Konita Lutfiyah; Lukita Pasha; Lily Maria
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1101

Abstract

The rapid expansion of smart spaces has increased the need for robotic systems capable of interpreting visual cues, recognizing human behavior, and responding safely in real time. However, existing vision-based models often struggle with occlusion, lighting variation, latency constraints, and limited contextual understanding in dynamic human-centered environments. This study develops a hybrid vision-based pattern recognition framework that integrates Convolutional Neural Networks (CNNs), Transformer-based attention mechanisms, multi-scale feature fusion, supervised learning, and reinforcement learning. The model is trained and validated using publicly available human–robot interaction datasets and simulated smart space scenarios involving gesture recognition, object detection, activity recognition, and intention prediction. The objective is to enhance intelligent human–robot interaction by improving visual perception accuracy, contextual interpretation, adaptive decision-making, and real-time responsiveness in smart environments. The proposed framework achieves stronger performance than baseline CNN-only and Vision Transformer models, with improved accuracy in gesture recognition, object detection, activity recognition, and intention prediction while maintaining low-latency inference suitable for real-time robotic interaction. The model also demonstrates better adaptability under dynamic lighting, occlusion, and multi-person interaction scenarios. This study concludes that combining CNN-based local feature extraction, Transformer-based global attention, and reinforcement learning-based policy optimization provides a reliable, adaptive, and context-aware framework for intelligent robotic systems. The findings support safer and more efficient human–robot collaboration in healthcare, smart homes, collaborative workplaces, and smart city environments.
Co-Authors Harry Agustian Henry Henry Lily Maria Lukita Pasha Muhamad Faizal Fazri Nicholas Lachlan