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International Transactions on Artificial Intelligence (ITALIC)
Published by Pandawan Sejahtera Indonesia
ISSN : 29636086     EISSN : 29631939     DOI : https://doi.org/10.33050/italic
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Library & Information Science
International Transactions on Artificial Intelligence (ITALIC) is an international, open-access journal established to publish groundbreaking research in the field of Artificial Intelligence (AI). ITALIC focuses on both theoretical and experimental AI research and explores its applications across various interdisciplinary fields. The journal places a strong emphasis on emerging technologies that contribute to sustainable development, in line with the United Nations Sustainable Development Goals (SDGs). ITALIC welcomes contributions that cover a wide range of AI applications, including machine learning, neural networks, natural language processing, AI in energy management, sustainability, and urban infrastructure. In addition to original research, the journal publishes reviews, mini-reviews, case studies, and commentaries, fostering dynamic discussions on the evolving role of AI in addressing global challenges. All submissions are rigorously reviewed through a double-blind peer-review process, ensuring high academic standards. As an open-access journal, ITALIC makes its content freely available to a global audience, enhancing the dissemination of critical insights. Each article is assigned a Digital Object Identifier (DOI), ensuring permanent access and easy referencing.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 68 Documents
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Application of AI in Optimizing Energy and Resource Management: Effectiveness of Deep Learning Models Agus Kristian; Thomas Sumarsan Goh; Ahmad Ramadan; Archa Erica; Sondang Visiana Sihotang
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.530

Abstract

In the era of globalization and rapid industrial growth, energy efficiency and resource management are crucial to addressing complex environmental and economic challenges. Efficient management reduces costs and contributes to sustainability. Technological advancements in Artificial Intelligence (AI) enhance energy efficiency and resource management through faster data analysis, better predictions, and automation. Despite progress, challenges like inaccurate energy demand predictions and inefficient resource allocation persist. This study explores AI's role in improving energy and resource management efficiency, focusing on prediction, optimization, and automation using Deep Learning approaches, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The findings show that AI models significantly enhance efficiency and sustainability by providing accurate predictions and automation recommendations. This research underscores AI's practical relevance, suggesting companies integrate these technologies to optimize energy use and achieve sustainability goals.
Optimizing Electrical Energy Use through AI: An Integrated Approach for Efficiency and Sustainability Heni Nurhaeni; Ariana Delhi; Ora Plane Maria Daeli; Sheila Aulia Anjani; Natasya Aprila Yusuf
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.533

Abstract

The increasing need for electrical energy in the modern era requires innovative steps to optimize its use. This research explores the application of Artificial Intelligence (AI) in optimizing the use of electrical energy with a focus on efficiency and sustainability. Using a quantitative approach with a descriptive-analytical design, this research collects data from various sources, including surveys, interviews and secondary literature. The results show that the application of AI can reduce electrical energy consumption by 20-30% in various sectors, such as the manufacturing industry and smart households. In addition, AI contributes significantly to reducing carbon emissions, with a 25% reduction in emissions in the manufacturing industrial sector. AI also demonstrated higher energy efficiency compared to traditional methods, with an average improvement of 25%. These findings imply that AI not only improves energy efficiency but also supports environmental sustainability through reducing carbon emissions. Practical recommendations include investment in AI technologies for energy management and policy support to accelerate AI adoption. This research provides the basis for further studies to explore the potential of AI in other sectors and its long-term economic impact. Thus, the application of AI in electrical energy management is expected to contribute significantly to energy efficiency and global sustainability.
Leveraging AI for Superior Efficiency in Energy Use and Development of Renewable Resources such as Solar Energy, Wind, and Bioenergy Umi Rusilowati; Hajra Rasmita Ngemba; Rio Wahyudin Anugrah; Anandha Fitriani; Eka Dian Astuti
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.537

Abstract

Energy efficiency and the development of renewable resources are crucial issues in addressing the global energy crisis and climate change. This research explores the role of artificial intelligence (AI) in increasing energy efficiency and optimizing the development of renewable resources, such as solar energy, wind, and bioenergy. By using a mixed-methods approach that combines qualitative and quantitative methods, this research identifies concrete applications of AI in various renewable energy sectors. The results demonstrate that AI can significantly improve operational efficiency and reduce energy waste. Examples include optimizing solar panel placement, predictive maintenance of wind turbines, and optimizing fermentation processes in biogas production. The implementation of AI in renewable energy not only enhances efficiency but also reduces costs and supports sustainability. This research contributes to the field of energy efficiency and AI technologies by providing empirical evidence of the benefits of AI in the renewable energy sector. It is recommended that governments and the energy industry widely adopt AI, invest in technology and workforce training, and strengthen collaboration between the energy, technology, and academic sectors to develop innovative and applicable AI solutions. Further research should conduct broader and more comprehensive studies, including analysis of the long-term costs and benefits of AI implementation, as well as the integration of AI technology with existing energy management systems.
AI-Based Strategies to Improve Resource Efficiency in Urban Infrastructure Ninda Lutfiani; Nuke Puji Lestari Santoso; Ridhuan Ahsanitaqwim; Untung Rahardja; Achani Rahmania Az Zahra
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.545

Abstract

Rapid urbanization has significantly increased urban populations, leading to higher consumption of resources such as energy, water, and fuel. Resource efficiency is crucial to managing urban growth in an environmentally friendly and economical manner. This research aims to explore the role of artificial intelligence (AI) in improving resource efficiency in urban infrastructure. By leveraging AI technology, this study seeks to find innovative solutions that can optimize resource use, enhance energy management, and improve monitoring and control of infrastructure systems. The findings indicate that the implementation of AI can increase energy efficiency by 15%, reduce transportation travel times by 15%, and improve water management efficiency by 15%. These results demonstrate that AI can be an effective tool in supporting the sustainability of urban infrastructure, reducing operational costs, and mitigating environmental impacts. This research provides practical guidance for city managers and policymakers in designing and implementing smarter and more efficient technological solutions.
AI as a Driver of Efficiency in Waste Management and Resource Recovery Li Wei Ming; James Anderson; Farhan Hidayat; Firdaus Dwi Yulian; Nanda Septiani
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.547

Abstract

Effective waste management and resource recovery are essential for maintaining environmental sustainability. With the increasing volume of waste generated from industrial and domestic activities, there is a critical need for strategies that reduce environmental impact and enhance resource utilization efficiency. This study explores the application of artificial intelligence (AI) technologies, specifically Machine Learning (ML) and Artificial Neural Networks (ANN), in optimizing waste management processes. The research demonstrates that AI can significantly improve waste classification accuracy, predict waste volumes, and identify resource recovery opportunities. Implementing AI solutions resulted in a 15% increase in resource recovery efficiency and a 20% reduction in operational costs. These findings provide valuable insights for stakeholders and policymakers in integrating AI technologies to achieve more sustainable waste management practices.
Improving Natural Resource Management through AI: Quantitative Analysis using SmartPLS Juan Carlos Rodr ́ıgue; John van der Merwe; Syahrul Muarif Wahid; Galih Putra Cesna; Dimas Aditiya Prabowo
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.548

Abstract

This study evaluates the role of Artificial Intelligence (AI) in enhancing the efficiency of natural resource management through a quantitative analysis using SmartPLS. Data was collected from 200 professionals with significant experience in AI and natural resource management. Descriptive statistics indicated high levels of AI usage (X1) and technological competence (X2) among respondents, with average scores of 4.2 and 4.0, respectively. Convergent and discriminant validity were confirmed, with all constructs having factor loading values above 0.7 and AVE exceeding 0.5. Structural model analysis revealed that AI usage and technological competence positively and significantly impact natural resource management efficiency (Y1), with path coefficients of 0.45 and 0.38, respectively. These findings underscore AI's critical role and the necessity of technological training to maximize its benefits. This research contributes to the literature by highlighting the importance of integrating AI in sustainable resource management practices, providing a robust framework for future studies.
AI for Sustainable Development: Applications in Natural Resource Management, Agriculture, and Waste Management Jack Jones; Edward Harris; Yusuf Febriansah; Alfri Adiwijaya; Ihsan Nuril Hikam
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.549

Abstract

The integration of artificial intelligence (AI) into sustainable development practices holds significant promise for addressing contemporary environmental, economic, and social challenges. This paper explores the application of AI in natural resource management, sustainable agriculture, and waste and energy management. The study employs a mixed-methods approach, combining qualitative analysis of case studies with quantitative data analysis to evaluate the effectiveness of AI technologies. Findings indicate that AI significantly enhances efficiency and effectiveness across various domains, including improved resource monitoring, optimized agricultural practices, and enhanced waste management processes. The results underscore AI's potential in mitigating climate change and promoting biodiversity through advanced predictive models and monitoring systems. This research highlights the critical role of supportive policies and infrastructure in realizing AI's benefits for sustainable development. The study concludes with recommendations for policymakers to foster AI adoption and address challenges such as high initial costs and data privacy concerns.
The Role of Artificial Intelligence in Sustainable Agriculture and Waste Management: Towards a Green Future Daniel Hernandez; Lukita Pasha; David Arian Yusuf; Rifky Nurfaizi; Dwi Julianingsih
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.552

Abstract

This study explores the application of artificial intelligence (AI) in achieving sustainable development goals, focusing on sustainable agriculture and waste management. Using a mixed-methods approach, we analyzed data from various case studies and conducted a comprehensive literature review. Our findings reveal that AI significantly enhances operational efficiency, resource optimization, and cost reduction across these sectors. For instance, AI-powered smart irrigation systems in India have reduced water usage by 30% while increasing crop yields, and AI applications in Singapore's waste management have improved recycling rates by 25%. Despite these benefits, challenges such as infrastructure limitations, the need for specialized technical skills, and societal resistance persist. By conducting in-depth interviews with experts and surveys with practitioners, we gathered extensive data that underscores the need for supportive policies, infrastructure investment, and comprehensive training programs to maximize AI's potential. Our research provides practical recommendations to overcome these challenges, aiming to fully leverage AI's capabilities for a greener, more sustainable future.
Transforming Energy and Resource Management with AI: From Theory to Sustainable Practice Zaharuddin; Sipah Audiah; Yulia Putri Ayu Sanjaya; Ora Pertiwi Daeli; Michael Johnson
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.554

Abstract

Efficient and sustainable energy management is crucial for addressing global environmental challenges. Artificial intelligence (AI) has emerged as a significant tool in the energy revolution, enhancing operational efficiency and integrating renewable energy sources. This study examines the impact of AI on optimizing energy and resource management, focusing on increasing renewable energy use and efficiency. Using a quantitative and exploratory approach, data from 100 energy companies that have implemented AI solutions were analyzed. The findings show that AI can improve energy efficiency by 25%, strengthen sustainable operations, and reduce environmental impact. These results align with Complex Systems Theory, highlighting that advanced technologies like AI enhance system adaptability and efficiency. Despite these insights, the study is limited to companies that have adopted AI and focuses solely on the energy sector. This highlights the need for broader research across various sectors and geographic contexts. The implications suggest that AI not only improves energy management but also supports global sustainability efforts, making it vital for a sustainable energy future.
Enhancing User Login Efficiency via Single Sign-On Integration in Internal Quality Assurance System (eSPMI) Maulana Yusuf; Muhamad Yusup; Reza Dani Pramudya; Ahmad Yadi Fauzi; Agung Rizky
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.556

Abstract

In the continually evolving digital era, authentication system efficiency has become crucial for accelerating processes and enhancing user security. This research aims to analyze the integration of Single Sign-On (SSO) features in authentication systems and its impact on user login efficiency, incorporating Artificial Intelligence (AI) concepts. Using a comparative analysis between traditional authentication systems and those integrated with SSO, sample data from three major technology companies show that SSO integration reduces average login time by 60% and increases user satisfaction by 70%. Additionally, integrating AI in SSO systems enhances security by providing predictive analytics for potential security threats and optimizing the overall user experience. These findings suggest that broader adoption of AI-enhanced SSO can significantly strengthen security and efficiency in corporate authentication systems, making it a valuable strategy for organizations aiming to improve user satisfaction and data protection.

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