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All Journal Jurnal Teknik Informatika C.I.T. Medicom International Journal of Basic and Applied Science Journal Basic Science and Technology CEBONG Journal International Journal of Enterprise Modelling
Rizky A, Galih Prakoso
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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AI-Based Sentiment Analysis of Social Media to Detect Public Opinion on Government Policies Rizky, Galih Prakoso; Alrasyid, Wildan
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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Abstract

In the digital age, social media has become a powerful platform for public expression and discourse, offering governments a real-time window into citizen sentiment. This research explores the application of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) techniques, to analyze public sentiment on social media in response to government policies. Using data primarily sourced from Twitter, the study applies a BERT-based sentiment analysis model to classify public reactions into positive, negative, and neutral categories. The model achieved high performance with an accuracy of 89.2%, precision of 88.6%, and recall of 87.9%, outperforming traditional classifiers. Sentiment was analyzed across three key policy areas: fuel subsidy removal, education curriculum reform, and COVID-19 vaccination programs. Results indicate significant variations in public sentiment based on policy type, timing, and inferred demographic factors. A real-time sentiment analysis dashboard was developed to support policymakers in monitoring public opinion trends and improving communication strategies. This study demonstrates the potential of AI-driven sentiment analysis as a tool for enhancing data-informed governance, public engagement, and policy responsiveness.
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.
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.
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.
Smart City Weather and Disaster Monitoring Architecture: LoRaWAN Integration with COBIT 2019 Governance Yulistiawan, Bambang Saras; A, Galih Prakoso Rizky; Widyastuti, Rifka; Mulianingtyas, RR Octanty
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1267.pp59-74

Abstract

Climate change, urbanization, and the increasing frequency of natural disasters such as floods and forest fires demand that Indonesian cities adopt real-time, integrated, and reliable environmental monitoring systems. Within the context of smart cities, LoRaWAN technology offers wide coverage, low power consumption, and cost-efficient operations, making it highly relevant for city-scale multi-sensor monitoring systems. This study proposes the design of a LoRaWAN-based weather and disaster monitoring system architecture integrated into the smart city framework, while simultaneously adopting the IT governance principles of COBIT 2019. The methodology includes a literature review and the mapping of five COBIT domains (EDM03, APO03, BAI03, DSS02, MEA01) to LoRaWAN’s technical components, ranging from sensors, gateways, and network servers to application servers, dashboards, and public notification modules. The analysis demonstrates that the proposed design enhances data standardization, end-to-end security, monitoring, scalability, and device governance. The integration of COBIT 2019 further enables the optimization of risk management, monitoring effectiveness, incident response, and regulatory compliance. In conclusion, the proposed architecture provides a comprehensive framework to support resilient, adaptive, and sustainable smart cities. However, this architecture has not yet been implemented in practice, thus necessitating further implementation and evaluation to ensure the system’s effectiveness and sustainability in operational environment.
A New Framework for IT Governance Excellence Yulistiawan, Bambang Saras; Mulianingtyas, Rr Octanty; Widyastuti, Rifka; A , Galih Prakoso Rizky
International Journal of Enterprise Modelling Vol. 19 No. 3 (2025): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

The rapid digital transformation requires an organization to have adaptive, integrated governance and management of information technology services (IT). However, two popular frameworks, COBIT and ITIL, have weaknesses when implemented separately. COBIT seems too normative and strategic, while ITIL is too operational and procedural; therefore, both of them fail to align the requirements between the strategic direction and information technology service execution. This study proposes the CITIGOV Model, an integrative framework that aligns the strengths of COBIT and ITIL in a model of modular and adaptive governance and information technology service. With three main domains, Strategic Governance, Service-Oriented Management, and Continuous Value Optimization, and seven elements of sustainable operations in IT governance. This study employs the Design Science Research method and has been validated through a literature review, theoretical analysis, and the mapping of modern digital organisation needs. The result of this study not only delivers theoretical contributions to IT framework integration, but also the practical implications as well as relevant guidance implementation and applicability in the context of public or private organisations. Keywords:
A bayesian dynamic latent state model for predicting infant sleep-wake patterns under daily massage intervention A , Galih Prakoso Rizky; Rasenda, Rasenda; Dermawan, Budi Arif; Arifuddin, Nurul Afifah; Alrasyid , Wildan
International Journal of Basic and Applied Science Vol. 14 No. 1 (2025): Computer Science, Engineering, Basic and Applied mathematics Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i1.699

Abstract

Sleep disturbances in infants present a persistent challenge for caregivers and healthcare providers. This study proposes a Bayesian Dynamic Latent State Model to predict infant sleep-wake patterns in response to daily massage, a non-pharmacological intervention. The model captures latent sleep propensity as a dynamic hidden process influenced by current and previous massages, individual random effects, and autoregressive components. Observed outcomes include nocturnal sleep duration and nighttime awakenings, modeled using Gaussian and Poisson distributions respectively. Through numerical simulations and a real-world case study, the model demonstrates clear benefits: average nocturnal sleep duration increased by approximately 1.2–1.5 hours, while nighttime awakenings decreased by about 35–40% on intervention days, with residual improvements on subsequent days. Compared to traditional static and time-series models, the proposed Bayesian approach provides greater flexibility in handling uncertainty, explicitly models carry-over effects, and integrates individual heterogeneity in sleep responses contributions that have not been fully addressed in prior infant sleep studies. This research thus advances the scientific understanding of dynamic, intervention-driven sleep processes, while also offering practical implications for evidence-based pediatric nursing and personalized infant care strategies. While promising, validation is currently limited to a small dataset and simplified assumptions. Future work will involve larger-scale testing, incorporation of additional external factors, and benchmarking against alternative machine learning models.
Co-Authors Alrasyid, Wildan Bambang Saras Yulistiawan Budi Arif Dermawan Mulianingtyas, RR Octanty Nurul Afifah Arifuddin Rasenda, Rasenda Rifka Widyastuti, Rifka