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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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Big Data Analytics for Predictive Insights in Healthcare Gates, John Doe; Yulianti, Yulianti; Pangilinan, Greian April
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.622

Abstract

This study leverages the transformative power of big data analytics to enhance healthcare outcomes by integrating diverse data sources like electronic health records, medical imaging, and genomic data to refine predictive models that forecast disease progression and personalize treatment strategies. Employing rigorous data management and machine learning, our findings demonstrate effective risk factor identification and resource optimization, significantly reducing hospital readmissions and improving chronic disease management as evidenced by a case study at City Hospital. Despite challenges related to data security and integration, the research aligns with United Nations SDGs, particularly SDG 3 (Good Health and Well-being) and SDG 9 (Industry, Innovation, and Infrastructure), highlighting the role of analytics in promoting health equity and operational efficiency. The study advocates for the expanded use of big data to build a sustainable, resilient healthcare infrastructure responsive to diverse population needs, recommending that healthcare providers and policymakers utilize these insights to propel data-driven, patient-centric solutions, furthering progress towards global health goals and sustainable development. Future research should include emerging data streams like social determinants of health to enrich these models, ensuring ongoing advancements in healthcare analytics.
Smart Grids: Integrating AI for Efficient Renewable Energy Utilization Noviati, Nuraini Diah; Maulina, Sondang Deri; Smith, Sarah
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.644

Abstract

The urgent global shift from fossil fuels to renewable energy sources necessitates innovative solutions to address energy system management challenges. Smart grids, equipped with sophisticated infrastructures, play a crucial role in this transition. This study integrates Artificial Intelligence (AI) into smart grids to enhance their efficiency and reliability, directly supporting the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 11 (Sustainable Cities and Communities). Employing a mixed-methods approach, the research utilizes historical and real-time data, applying machine learning algorithms such as Linear Regression, Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long ShortxTerm Memory (LSTM) for predictive accuracy in energy management. Optimization techniques like Genetic Algorithms and Particle Swarm Optimization (PSO) are also implemented for resource scheduling and grid balancing. The results demonstrate significant improvements, with an 11.76% increase in energy efficiency and grid stability, a 66.67% reduction in prediction errors, and a 20% decrease in operational costs compared to conventional systems. These enhancements highlight the transformative potential of AI in smart grids, promoting more efficient and sustainable energy utilization. The study concludes that AI-driven smart grids are pivotal in achieving the SDGs by providing scalable and efficient solutions for renewable energy integration, thereby fostering sustainable development and reducing environmental impacts.
AI-Powered Assistive Technologies for Improved Accessibility Brotosaputro, Goenawan; Supriyadi, Agung; Jones, Michael
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.645

Abstract

This research explores the effectiveness of AI-powered assistive technologies in enhancing accessibility for individuals with disabilities. The study combines qualitative and quantitative methods to evaluate the usability, efficiency, and user satisfaction of AI-integrated solutions compared to traditional assistive technologies. Findings indicate significant improvements in these areas, with AI-powered technologies reducing task completion times and increasing user satisfaction and communication efficiency. Case studies highlight diverse applications, such as AI-driven speech recognition and emotion recognition systems, demonstrating substantial benefits. Despite the promising results, the study acknowledges limitations such as small sample size and short-term focus, suggesting future research to explore long-term impacts, cost-effectiveness, and broader accessibility. This research contributes to the field of accessibility by providing empirical evidence of AI transformative potential, emphasizing the importance of personalized and adaptive support. Future developments should ensure sustainable and equitable implementation to maximize the benefits of AI-powered assistive technologies.
Improving Educational Outcomes Through Adaptive Learning Systems using AI Sari, Herva Emilda; Tumanggor, Benelekser; Efron, David
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.647

Abstract

Adaptive learning systems powered by AI have transformed education by offering personalized learning experiences tailored to individual student needs, enhancing engagement and outcomes. This study examines the impact of AI-driven adaptive learning systems on educational outcomes across diverse settings using a mixed-methods approach. Quantitative data were collected through pre- and post-assessments, surveys, and system analytics, while qualitative insights were obtained via interviews. Participants included 300 students and 50 educators spanning primary to higher education. Findings revealed a substantial improvement in student performance, with average post-assessment scores increasing from 68.4 to 82.7. AI tools such as Smart Sparrow and IBM Watson Education demonstrated higher course completion rates and increased student engagement. Comparative analysis confirmed the superior effectiveness of adaptive systems over traditional methods. These results highlight the potential of AI-driven systems to enhance educational quality and equity. The study also identifies challenges, including institutional technical readiness, educator training, and infrastructural needs, which are critical for successful implementation. Future research should explore long-term impacts, algorithmic optimization, and ethical considerations, addressing issues such as potential biases and data privacy concerns. Standardizing references, citations, and formatting is recommended to ensure professional presentation. By examining the practical barriers and offering insights into their resolution, this research provides a foundation for the broader adoption of adaptive learning systems, underscoring their transformative potential in creating inclusive and effective educational environments. These findings advocate for continued exploration and development of AI-driven tools to advance learning outcomes globally.
Artificial Intelligence in Environmental Monitoring: Predicting and Managing Climate Change Impacts Bianchi, Olivia; Putro, Herman Purwoko
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.650

Abstract

Environmental monitoring has become increasingly critical as climate change continues to pose significant global challenges, impacting ecosystems, economies, and human health. Predicting and managing these impacts requires advanced technological solutions, and Artificial Intelligence (AI) has emerged as a powerful tool in this domain. This study aims to explore the integration of AI techniques, such as machine learning and deep learning, into environmental monitoring to enhance the accuracy of climate change impact predictions and improve management strategies. The methods employed include the application of Convolutional Neural Networks (CNN) for land cover classification and Long Short-Term Memory (LSTM) models for forecasting air quality levels. The results indicate that AI significantly improves prediction accuracy, with CNN achieving high performance in land classification and LSTM models providing reliable forecasts for air quality changes. The findings suggest that AI can be instrumental in transforming environmental monitoring, enabling more proactive and data-driven climate change management. Future research should focus on improving data quality, model interpretability, and expanding AI applications in various environmental contexts.
AI and Blockchain Integration: Enhancing Security and Transparency in Financial Transactions Martinez, Daniel; Magdalena, Lena; Savitri, Agnes Novalita
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.651

Abstract

The integration of Artificial Intelligence (AI) and Blockchain is revolutionizing the financial sector, targeting crucial challenges like security and transparency. This paper explores the synergistic effects of AI and Blockchain on enhancing the security of financial transactions through advanced real-time fraud detection, anomaly identification, and decentralized transaction verification. Employing a comprehensive review of existing literature and case studies, the research investigates how AI’s capabilities in processing vast data volumes can be leveraged alongside Blockchain’s robust, immutable ledger system to mitigate risks in financial operations effectively. The findings reveal that integrating AI with Blockchain not only significantly improves the security by enabling the real-time detection of anomalies but also upholds the integrity and transparency of transactions across distributed ledgers. The results underscore the potential of AI-Blockchain technology to enhance financial transaction frameworks and highlight its capacity to support the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 16 (Peace, Justice, and Strong Institutions) by fostering more transparent and secure economic environments. The conclusion of the study suggests further research on the scalability of AI-Blockchain integrations and their broader application across various industries, pointing towards a transformative impact on global financial practices.
Integrating Machine Learning with Web Intelligence for Predictive Search and Recommendations Santiago, Maria; Febiansyah, Hidayat; Dinarwati, Dini
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.654

Abstract

This study examines the integration of Machine Learning (ML) with Web Intelligence (WI) as a transformative approach for enhancing web-based search and recommendation systems. The objective is to utilize the combined strengths of ML and WI to significantly increase the accuracy, precision, and relevance of predictions, providing personalized and context-aware results that adapt in real-time. Employing a hybrid model that leverages both the predictive capabilities of ML and the dynamic adaptability of WI, this research methodologically assesses the performance against traditional models through rigorous testing. Results indicate that the integrated system substantially outperforms conventional models, demonstrating enhanced performance metrics across accuracy, precision, and recall. Theoretically, this integration contributes to the advancement of WI frameworks, while practically, it offers significant improvements for real-world applications, especially in optimizing user interactions and satisfaction. However, the study also recognizes limitations related to the scalability of the data and models used. Future research should focus on refining model complexity and enhancing real-time data processing capabilities. Additionally, the integration of these technologies supports several Sustainable Development Goals (SDGs), particularly Goal 9 (Industry, Innovation, and Infrastructure) by promoting sustainable industrialization through advanced technologies, Goal 8 (Decent Work and Economic Growth) by fostering economic growth and employment in the tech sector, and Goal 12 (Responsible Consumption and Production) by enabling more informed consumer choices through better recommendations. These connections underline the role of innovative technologies in achieving sustainable development and enhancing global economic and social frameworks.
The Future of Adaptive Machine Learning Algorithms in High-Dimensional Data Processing Wilson, Anne; Anwar, Muhammad Rehan
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.656

Abstract

This study investigates the potential of adaptive machine learning algorithms for processing high-dimensional data across various fields, directly supporting the advancement of the United Nations Sustainable Development Goals (SDGs) such as healthcare, economic growth, and sustainable cities. The core objectives are to critically review existing methods, tackle the challenges posed by large datasets, and project future developments in adaptive machine learning technologies. Through a comprehensive analysis of diverse algorithms including autoencoders, deep learning, reinforcement learning, and ensemble methods this research evaluates their efficacy in managing the complexities of large-scale data. Results demonstrate that while deep learning models provide the highest accuracy, they also demand considerable computational resources. Conversely, ensemble methods and autoencoders show competitive performance with greater efficiency, although reinforcement learning exhibits adaptability at the cost of reduced scalability. The findings advocate for enhanced focus on improving the efficiency, generalization capabilities, and interpretability of these algorithms to better accommodate the increasing complexity of data-driven environments. Promising applications identified include enhancing diagnostic accuracy in healthcare, optimizing financial analytics, and advancing autonomous system technologies. The study concludes that significant progress in adaptive machine learning will be crucial for achieving SDGs by enabling more effective and efficient data analysis solutions, thereby fostering sustainable development across multiple domains.
Integrating Artificial Intelligence for Autonomous Navigation in Robotics Costa, Pedro; Ferdiansyah, Januri; Ariessanti, Hani Dewi
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.657

Abstract

This research examines the integration of Artificial Intelligence (AI) in enhancing autonomous navigation systems within robotics, focusing on developing adaptive machine learning algorithms for high-dimensional data processing. The primary objective is to advance AI-based navigation systems that outperform traditional methods in terms of accuracy, obstacle avoidance, and efficiency. By leveraging deep learning for intricate visual perception and reinforcement learning for agile decision-making and path optimization, the study achieves a substantial increase in navigation precision and obstacle detection in both simulated and real-world settings. The findings reveal that these AI-driven systems surpass conventional rule-based systems and exhibit superior adaptability in dynamic and unstructured environments. Future efforts will concentrate on refining these algorithms to enhance environmental recognition and extend AI applications to more complex robotic operations. This research supports Sustainable Development Goals (SDGs) by promoting innovative infrastructure (SDG 9) and fostering industry innovation and infrastructure development, which are vital for sustainable economic growth and environmental protection.
The Role of Machine Learning in Improving Robotic Perception and Decision Making Chen, Shih-Chih; Pamungkas, Ria Sari; Schmidt, Daniel
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.661

Abstract

Machine learning, specifically through Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL), significantly enhances robotic perception and decision-making capabilities. This research explores the integration of CNNs to improve object recognition accuracy and employs sensor fusion for interpreting complex environments by synthesizing multiple sensory inputs. Furthermore, RL is utilized to refine robots real-time decision-making processes, which reduces task completion times and increases decision accuracy. Despite the potential, these advanced methods require extensive datasets and considerable computational resources for effective real-time applications. The study aims to optimize these machine learning models for better efficiency and address the ethical considerations involved in autonomous systems. Results indicate that machine learning can substantially advance robotic functionality across various sectors, including autonomous vehicles and industrial automation, supporting sustainable industrial growth. This aligns with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 8 (Decent Work and Economic Growth), by promoting technological innovation and enhancing industrial safety. The conclusion suggests that future research should focus on improving the scalability and ethical application of these technologies in robotics, ensuring broad, sustainable impact.

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