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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer 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.
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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Abstract

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.
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 System dynamics quantitative model for enhancing e-government maturity in the indonesian education sector Yulistiawan, Bambang Saras; Widyastuti, Rifka; Mulianingtyas, Rr Octanty; A, Galih Prakoso Rizky; Sihotang, Hengki Tamando
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Sep (In Progress)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.693

Abstract

This study develops a deterministic mathematical model integrated with system dynamics to measure key success factors driving e-government maturity in Indonesia’s education sector. Addressing the gap in previous research, which mainly relied on descriptive methods, the model quantitatively examines causal relationships among leadership commitment, budget support, digital infrastructure, human capital, service quality, and feedback mechanisms. The methodology involves three stages: (1) constructing a causal loop diagram based on theoretical and empirical insights, (2) converting these relationships into a linear system of equations normalized on a [0–1] scale, and (3) performing simulations and sensitivity analyses to evaluate policy scenarios. Simulation results indicate that even relatively high leadership commitment (K=0.75) only produces moderate maturity levels (M≈0.409). The greatest improvement occurs when feedback loops are reinforced and service quality investments are prioritized. Sensitivity analysis reveals the model is particularly responsive to changes in feedback effectiveness and service quality weighting, identifying these as critical leverage points for accelerating transformation. Under optimal conditions, maturity can increase from 0.41 to 0.48, reflecting a 7% gain over the baseline. The study contributes a replicable quantitative framework for evidence-based policymaking, while noting limitations in parameter assumptions and empirical calibration for future refinement.
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.
Developing the Adaptive Digital IT Governance Framework for Next-Generation IT Governance Yulistiawan, Bambang Saras; Widyastuti , Rifka; Mulianingtyas , RR Octanty; A, Galih Prakoso Rizky; Sihotang, Hengki Tamando
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5628

Abstract

The increasing complexity of digital transformation requires an adaptive, measurable, and contextaware IT governance model. However, existing frameworks such as COBIT, ITIL, TOGAF, and ISO/IEC 38500 tend to be partial and prescriptive, failing to address strategic, operational, and innovative needs holistically. This study proposes the Adaptive Digital IT Governance Framework, anovel governance model synthesized from eleven leading IT frameworks and structured into three integrated domains: Govern, Manage, and Adapt. Employing a Design Science Research methodology, the model was developed through a systematic framework analysis, conceptual domain formulation, iterative implementation mapping, and the design of a maturity assessment instrument. The results demonstrate that the Adaptive Digital IT Governance Framework offers a modular, scalable, and value-driven governance solution suited for diverse organizational contexts. Theoretical contributions include extending the IT governance paradigm by integrating strategic alignment, agile governance, and digital sustainability. Practically, the framework provides actionable guidance for designing, assessing, and enhancing digital governance systems across sectors. Unlike previous cross-framework synthesis efforts, the Adaptive Digital IT Governance Framework explicitly introduces the Adapt domain, operationalizing governance agility, innovation capability, and sustainability measurement. This makes the Adaptive Digital IT Governance Framework the first modular, maturity-oriented framework that simultaneously integrates strategy, operations, and adaptability, positioning it as a next-generationmodel to support organizational resilience and sustainable digital transformation.
Co-Authors Alrasyid, Wildan Bambang Saras Yulistiawan Budi Arif Dermawan Hengki Tamando Sihotang Mulianingtyas , RR Octanty Mulianingtyas, RR Octanty Nurul Afifah Arifuddin Rasenda, Rasenda Rifka Widyastuti, Rifka Widyastuti , Rifka