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INDONESIA
CEBONG Journal
Published by Ihsa Institute
ISSN : 2828366X     EISSN : 2828366X     DOI : https://doi.org/10.35335/cebong
Core Subject : Economy, Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Industrial & Manufacturing Engineering
CEBONG Journal adalah jurnal ilmiah akses terbuka, peer-review, dan ilmiah yang diterbitkan oleh IHSA Institute, Indonesia. Tujuan dari CEBONG Journal adalah untuk mempublikasikan artikel penelitian asli dari para peneliti dan praktisi tentang berbagai topik Green Economy dan Blue Economy: energi, lanskap, infrastruktur, Sumberdaya Perairan dan Kelautan, Teknologi Inforamasi dan Komunikasi (TIK), Digital Economic
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 86 Documents
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The Impact of Virtual Reality (VR) Technology on Enhancing Customer Engagement in the Online Travel Industry Fransisco, Tio Acho
Cebong Journal Vol. 3 No. 3 (2024): July: Green dan Blue Economy
Publisher : IHSA Institute

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Abstract

This research investigates the impact of Virtual Reality (VR) technology on customer engagement within the online travel business. With the rapid advancements in VR, this study explores how immersive and interactive VR experiences influence customer behavior and decision-making in the travel industry. Using a mixed-methods approach, including surveys, interviews, case studies, and experiments, the research evaluates the effectiveness of VR compared to traditional engagement methods. The findings reveal that VR significantly enhances customer engagement by providing a higher level of immersion and interactivity. Participants who interacted with VR content reported increased emotional connection and longer engagement compared to those exposed to static content and traditional multimedia. Empirical data also demonstrates that VR experiences lead to higher booking rates and conversion metrics, as users are more likely to complete bookings after engaging with VR previews of destinations and accommodations. The study highlights the growing importance of personalization in VR experiences, with advancements allowing for more tailored and customized virtual tours.
Artificial Intelligence: Optimizing The Recruitment Process Abdurrahim, Abdurrahim
Cebong Journal Vol. 4 No. 1 (2024): Nov: Green dan Blue Economy
Publisher : IHSA Institute

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Abstract

This study aims to explore the role and potential of artificial intelligence (AI) in the recruitment process in an increasingly complex digital era. Using the Narrative Literature Review method, this research summarizes and analyzes various relevant literature sources related to the use of AI in recruitment. The findings indicate that AI enhances efficiency and effectiveness in candidate search and selection, with the ability to analyze data quickly and accurately. Additionally, AI contributes to reducing bias in decision-making, creating a more objective and transparent process. However, challenges such as potential algorithmic bias and privacy issues still need to be addressed. With proper implementation, AI not only helps organizations find suitable candidates but also promotes a fairer and more inclusive recruitment process. This study provides valuable insights for HR practitioners and stakeholders in adopting AI technology in recruitment.
Evaluation of the Influence of Thermal Comfort (PMV) of Classrooms on Teachers' Work Stress Levels in Dry Tropical Areas (Study at SDK Maumere II, Sikka Regency) Da Mendez, Martina Rudolfa; Tandafatu, Maria Carolina
Cebong Journal Vol. 4 No. 1 (2024): Nov: Green dan Blue Economy
Publisher : IHSA Institute

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Abstract

This study aims to analyze the effect of thermal comfort (PMV) in classrooms on the level of work stress of teachers in dry tropical areas, especially in SDK Maumere II, Sikka Regency. Quantitative research methods were used by involving all teachers as samples. Data collection was carried out by measuring the physical condition of the classroom and distributing questionnaires. The results showed that the thermal conditions in the classroom did not meet comfort standards, with PMV values ​​​​outside the comfort range. Correlation and regression analysis showed a significant relationship between thermal comfort and the level of work stress of teachers. The higher the level of thermal comfort, the lower the level of work stress experienced by teachers. The results of this study concluded that thermal comfort is an important factor that affects the level of work stress of teachers and needs to be considered in efforts to improve the quality of the work environment in schools
Exploring the Influence of Entrepreneurial Knowledge and Self-Confidence on Students' Entrepreneurial Interest: A Mixed-Methods Study Kurniawan, Dedi; Koswara, Dayat
Cebong Journal Vol. 4 No. 1 (2024): Nov: Green dan Blue Economy
Publisher : IHSA Institute

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Abstract

This research investigates the influence of entrepreneurial knowledge and self-confidence on students' entrepreneurial interest, aiming to deepen our understanding of the factors shaping entrepreneurial aspirations among the student population. Drawing on theoretical frameworks from social cognitive theory, theory of planned behavior, and human capital theory, the study examines the relationships between entrepreneurial knowledge, self-confidence, and entrepreneurial interest through a mixed-methods approach. Quantitative data is collected through surveys administered to undergraduate and graduate students across various educational disciplines, supplemented by qualitative insights from semi-structured interviews. The analysis reveals significant positive associations between entrepreneurial knowledge, self-confidence, and entrepreneurial interest, highlighting the importance of entrepreneurship education and self-belief in fostering students' entrepreneurial aspirations. The findings underscore the need for educational institutions to integrate entrepreneurship education into curricula, provide experiential learning opportunities, and foster supportive learning environments to cultivate students' entrepreneurial talents and ambitions. Moreover, the study highlights the critical role of self-confidence in empowering students to pursue entrepreneurial ventures with confidence and resilience. Overall, this research contributes to the existing body of knowledge on entrepreneurship education and psychological factors influencing entrepreneurial behavior, providing valuable insights for policymakers, educators, and program administrators seeking to foster a culture of innovation and entrepreneurship among students.
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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Abstract

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.
Arsitektur Enterprise Berbasis Cloud dan IoT untuk Mendukung Smart Governance Yulistiawan, Bambang Saras
Cebong Journal Vol. 4 No. 1 (2024): Nov: Green dan Blue Economy
Publisher : IHSA Institute

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Abstract

Arsitektur Enterprise Berbasis Cloud dan IoT untuk Mendukung Smart Governance Pertumbuhan teknologi digital yang cepat telah mengubah sistem tata kelola di seluruh dunia, menciptakan peluang dan tantangan baru bagi administrasi publik. Penelitian ini mengeksplorasi integrasi komputasi awan, Internet of Things (IoT), dan Enterprise Architecture (EA) sebagai kerangka kerja komprehensif untuk meningkatkan tata kelola digital. Dengan mensintesis literatur yang ada dan memeriksa studi kasus inisiatif kota pintar dan e-government, penelitian ini menyoroti bagaimana teknologi ini dapat secara kolektif meningkatkan efisiensi, transparansi, interoperabilitas, dan keterlibatan warga negara. Cloud Computing memberikan skalabilitas dan pemberian layanan yang fleksibel, IoT memungkinkan pemantauan data real-time untuk tata kelola yang responsif, dan EA memastikan penyelarasan terstruktur antara adopsi teknologi dan tujuan kelembagaan. Temuan menunjukkan bahwa sementara integrasi menawarkan manfaat substansial seperti peningkatan pengambilan keputusan, peningkatan kepercayaan warga negara, dan proses administrasi yang dirampingkan pemerintah juga menghadapi tantangan yang signifikan, termasuk risiko keamanan siber, masalah privasi data, kesenjangan infrastruktur, dan kesenjangan kebijakan. Analisis komparatif dengan penelitian sebelumnya menunjukkan bahwa kerangka kerja berbasis triad ini melampaui pendekatan teknologi tunggal sebelumnya, menawarkan model yang lebih holistik untuk transformasi digital yang berkelanjutan. Studi ini diakhiri dengan mengusulkan rekomendasi kebijakan dan menyarankan arahan penelitian di masa depan, khususnya di bidang integrasi kecerdasan buatan, kerangka kerja keamanan siber, dan evaluasi jangka panjang dari praktik tata kelola digital.
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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Abstract

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.
Pengembangan Model Tata Kelola Data Terintegrasi Berbasis Artificial Intelligence untuk Optimalisasi Business Intelligence Yulistiawan, Bambang Saras
Cebong Journal Vol. 4 No. 2 (2025): March: Green dan Blue Economy
Publisher : IHSA Institute

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Penelitian ini bertujuan untuk mengembangkan model tata kelola data terintegrasi berbasis Artificial Intelligence (AI) dalam rangka optimalisasi Business Intelligence (BI). Seiring dengan meningkatnya volume, variasi, dan kecepatan data di era digital, tata kelola data tradisional dinilai kurang mampu menjawab kebutuhan pengelolaan data yang kompleks, terutama dalam menjamin kualitas, konsistensi, dan integrasi lintas sistem. Model yang diusulkan dalam penelitian ini mengintegrasikan pendekatan AI untuk mendukung proses otomasi, peningkatan akurasi, serta efisiensi dalam pemrosesan dan analisis data. Hasil penelitian menunjukkan bahwa penerapan AI dalam tata kelola data mampu meningkatkan kualitas informasi yang dihasilkan BI, mempercepat proses pengambilan keputusan, serta memperkuat ketepatan strategi organisasi. Selain itu, model ini menawarkan fleksibilitas adaptif yang dapat disesuaikan dengan kebutuhan spesifik perusahaan tanpa mengabaikan standar keamanan dan privasi data. Namun, penelitian ini juga mengidentifikasi beberapa tantangan dan keterbatasan, antara lain terkait keamanan dan privasi data, kesiapan infrastruktur teknologi, resistensi organisasi terhadap adopsi AI, serta keterbatasan dataset untuk validasi model. Secara keseluruhan, penelitian ini berkontribusi dalam memperkuat kerangka teoretis dan praktis mengenai tata kelola data berbasis AI, sekaligus memberikan rekomendasi kebijakan dalam transformasi digital. Model yang dikembangkan diharapkan dapat menjadi acuan dalam implementasi BI yang lebih efektif, cerdas, dan berkelanjutan, sehingga mendukung peningkatan daya saing dan inovasi perusahaan di era ekonomi digital.
Model Tata Kelola Data Berbasis Artificial Intelligence untuk Mendukung Pengambilan Keputusan Strategis dalam Business Intelligence Yulistiawan, Bambang Saras
Cebong Journal Vol. 4 No. 3 (2025): July: Green dan Blue Economy
Publisher : IHSA Institute

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Abstract

Perkembangan era big data dan transformasi digital menuntut organisasi untuk mampu mengelola data secara efektif agar dapat mendukung pengambilan keputusan strategis. Penelitian ini bertujuan mengembangkan model tata kelola data berbasis Artificial Intelligence (AI) yang dapat meningkatkan kualitas, keamanan, dan kecepatan pengolahan data dalam sistem Business Intelligence (BI). Metodologi yang digunakan adalah design and development research dengan pendekatan mixed-method, mencakup kajian literatur, perancangan model, implementasi prototipe, serta evaluasi performa model melalui indikator kualitas data, efektivitas analisis, dan kemampuan pengambilan keputusan. Hasil penelitian menunjukkan bahwa model berbasis AI mampu melakukan validasi data, deteksi anomali, dan prediksi kebutuhan informasi secara otomatis dan real-time, sehingga meningkatkan akurasi, konsistensi, dan keandalan data. Integrasi AI dalam tata kelola data juga mempercepat proses analisis, mendukung pengambilan keputusan yang lebih cepat dan tepat, serta meningkatkan kemampuan BI dalam menghasilkan insight prediktif. Namun, implementasi model ini menghadapi tantangan terkait keamanan dan privasi data, keterbatasan infrastruktur teknologi dan sumber daya manusia, risiko bias algoritma, serta integrasi dengan sistem BI yang sudah ada. Penelitian ini memberikan kontribusi teoretis dengan memperluas literatur tata kelola data dan BI berbasis AI, kontribusi praktis bagi organisasi dalam meningkatkan kualitas pengambilan keputusan, serta kontribusi kebijakan terkait adopsi AI yang aman, etis, dan efektif. Model yang dikembangkan menjadi fondasi bagi organisasi untuk membangun sistem BI yang lebih adaptif, prediktif, dan berbasis data di era digital.
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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Abstract

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.

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