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Rizky A, Galih Prakoso
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Journal : CEBONG Journal

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Fundamentals of Machine Learning: Towards the Development of Intelligent Computational Models Rizky A, Galih Prakoso
Cebong Journal Vol. 4 No. 1 (2024): Nov: Green dan Blue Economy
Publisher : IHSA Institute

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This research examines the fundamental principles of machine learning (ML) and their significance in the development of intelligent computational models. By exploring core learning paradigms supervised, unsupervised, and reinforcement learning along with optimization strategies, model evaluation, and validation techniques, the study highlights how these elements collectively shape the effectiveness of ML applications. A review of existing literature over the past decade illustrates the rapid advancements in algorithms, architectures, and applications that have expanded the scope of computational intelligence across diverse domains such as healthcare, finance, and autonomous systems. The findings underscore that a clear understanding of ML fundamentals not only enhances real-world model performance but also provides a framework for guiding future research and innovation in intelligent systems. Despite these opportunities, the study also identifies challenges including data quality, interpretability, generalization, and ethical concerns, which must be addressed to ensure responsible and impactful implementation. Ultimately, this research concludes that the strength of intelligent computational models rests on their alignment with foundational ML principles, balancing technical progress with societal and ethical considerations.
Exploring Core Principles of Machine Learning for Advancing Intelligent Computing Paradigms Rizky A, Galih Prakoso
Cebong Journal Vol. 4 No. 2 (2025): March: Green dan Blue Economy
Publisher : IHSA Institute

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This research explores the core principles of machine learning (ML) as the foundation for advancing intelligent computing paradigms. As data-driven technologies rapidly evolve, ML has emerged as a central component in enabling adaptive, autonomous, and context-aware systems across various domains, from healthcare and finance to smart cities and industrial automation. Through a comprehensive review and analysis, the study examines fundamental ML techniques including supervised, unsupervised, reinforcement, and deep learning and evaluates their role in shaping computational intelligence. The methodology integrates conceptual analysis, synthesis of existing literature, and comparative evaluation of paradigms to highlight how ML differentiates itself from traditional algorithmic approaches. Findings reveal that ML not only enhances predictive accuracy and decision-making but also introduces new paradigms of adaptability, scalability, and self-learning, which are crucial for future intelligent systems. However, challenges such as data quality, interpretability, ethical concerns, and computational resource demands present limitations that must be addressed to ensure sustainable and responsible integration. This research contributes theoretically by refining the understanding of ML’s role in computational intelligence, practically by outlining its applications in real-world intelligent systems, and futuristically by framing new paradigms that combine technical advancement with ethical and policy considerations.
Theoretical Foundations of Machine Learning as a Pillar for Smart Computational Systems Rizky A, Galih Prakoso
Cebong Journal Vol. 4 No. 3 (2025): July: Green dan Blue Economy
Publisher : IHSA Institute

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This research explores the theoretical foundations of Machine Learning (ML) as a critical pillar for the development of smart computational systems. The study emphasizes the importance of core ML paradigms supervised, unsupervised, and reinforcement learning in providing the basis for intelligence, adaptability, and efficiency in modern computational models. By synthesizing theoretical insights with recent advancements, this research demonstrates how a deeper understanding of ML principles improves model design, reduces errors, and enhances the reliability of intelligent systems. The findings highlight that while ML theories significantly contribute to performance and innovation, challenges such as data bias, overfitting, interpretability, and computational limitations remain pressing concerns. Addressing these issues requires not only methodological improvements but also ethical and interdisciplinary approaches. In conclusion, this research affirms that ML theory is not merely academic but serves as a practical backbone for applied innovation, ensuring the development of systems that are robust, transparent, and sustainable. Future directions should focus on bridging theoretical advancements with real-world applications to strengthen the role of ML as a foundation for next-generation computational intelligence.
Reconfigurable Metasurface Panels for Active Electromagnetic Shielding of Protective Domes Sihotang, Hengki Tamando; Dermawan, Budi Arif; Rasenda, Rasenda; Rizky A, Galih Prakoso
Cebong Journal Vol. 4 No. 3 (2025): July: Green dan Blue Economy
Publisher : IHSA Institute

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The increasing complexity of electromagnetic (EM) environments in defense and communication systems necessitates shielding solutions that are both adaptive and efficient. Conventional static shielding domes, while effective in blocking electromagnetic interference (EMI), are inherently limited by their fixed frequency response, high structural weight, and lack of real-time adaptability. This research investigates the design and performance of reconfigurable metasurface panels for active electromagnetic shielding of protective domes, with the aim of enhancing shielding effectiveness, tunability, and structural efficiency. The study explores the integration of reconfigurable metasurfaces into dome architectures, enabling dynamic control of electromagnetic wave propagation through electronically tunable elements. Performance metrics including shielding effectiveness (in dB), tunable frequency ranges, angular stability, and real-time adaptability were evaluated and benchmarked against conventional static shielding designs. Results indicate that reconfigurable metasurface domes achieve superior shielding performance across wide frequency bands while offering significant weight reduction and improved adaptability. These characteristics make them well-suited for critical applications such as military radomes, satellite communication shelters, aerospace systems, and secure civilian infrastructures. However, challenges remain regarding large-scale fabrication, integration complexity, power requirements for active tuning, and environmental durability. Despite these limitations, the findings highlight the transformative potential of reconfigurable metasurfaces as the foundation of next-generation adaptive shielding technologies. This research demonstrates that reconfigurable shielding domes not only address the shortcomings of static designs but also pave the way for resilient, flexible, and future-proof electromagnetic protection systems.
Co-Authors Alrasyid, Wildan Bambang Saras Yulistiawan Budi Arif Dermawan Hengki Tamando Sihotang Mulianingtyas, RR Octanty Nurul Afifah Arifuddin Rasenda, Rasenda Rifka Widyastuti, Rifka Sihotang, Hengki Tamando