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All Journal Aptisi Transactions on Technopreneurship (ATT) ADI Journal on Recent Innovation (AJRI) International Transactions on Artificial Intelligence (ITALIC)
Pangilinan, Greian April
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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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.
Robotics and Automation with Artificial Intelligence: Improving Efficiency and Quality Hussain, Nazim; Pangilinan, Greian April
Aptisi Transactions On Technopreneurship (ATT) Vol 5 No 2 (2023): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v5i2.252

Abstract

The industrial and technological revolutions are accelerating globally as a result of the widespread adoption of new information and communication technologies like artificial intelligence (AI), the Internet of Things (IoT), and blockchain technology. Government, business, and academia are all paying close attention to artificial intelligence. In this study, a selection of well-read articles on artificial intelligence from recent publications is examined. The focus of this study is to offer an analysis of artificial intelligence using integrated industry information. It provides an overview of the extent of artificial intelligence using background information, motivating factors, technological advancements, and applications, as well as rational predictions for its future. This study can contribute to the field of artificial intelligence research and offer crucial knowledge to real-world practitioners. This study's key contribution is its clarification of the current state of the art in AI for further research. 
Technopreneurship and Market Feasibility of Modified Carrageenan Hydrogel for Industrial Heavy Metal Remediation Dulanlebit, Yeanchon Henry; Hernani, Hernani; Liliasari, Liliasari; Amran, Muhammad Bachri; Pangilinan, Greian April
Aptisi Transactions On Technopreneurship (ATT) Vol 8 No 1 (2026): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v8i1.622

Abstract

Heavy metal pollution in aquatic systems increases the need for sustainable and efficient natural adsorbent materials, and carrageenan extracted from Eucheuma cottonii, which contains O–H, S=O, C–O–S, and 3,6-anhydrogalactose functional groups, offers strong potential for binding metal ions. However, the adsorption efficiency of natural carrageenan for Copper (Cu2+) and Cadmium (Cd2+) ions remains limited, which frames the central problem of this study. In addressing this issue, the method employed involves synthesizing carrageenan hydrogel through alkaline extraction of Eucheuma cottonii and evaluating its adsorption capacity under controlled experimental conditions. The hydrogel was characterized and tested at pH 6–7, with a contact time of 90 minutes, an initial concentration of 200 mg/L, and an adsorbent mass of 6 g, followed by kinetic and isotherm modeling to analyze adsorption behavior. Based on the Findings, ionic exchange interactions between sulfate ester groups of K-carrageenan and metal cations significantly enhance adsorption performance, with the adsorption process following a pseudo-second-order kinetic model and the Freundlich isotherm providing the best fit R2 > 0.90, indicating heterogeneous multilayer adsorption. In the Conclusion, the chemically modified carrageenan hydrogel demonstrates effective adsorption of copper and cadmium ions and presents strong potential as an eco-friendly and efficient biomaterial for heavy metal remediation.
The Role of Big Data and Blockchain in Enabling Transparent and Sustainable Business Processes Amroni, Amroni; Darmawan, Afif Aditya; Pangilinan, Greian April
ADI Journal on Recent Innovation Vol. 6 No. 2 (2025): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v6i2.1202

Abstract

The integration of Big Data and Blockchain technologies is increasingly vital for businesses aiming to foster transparent and sustainable processes in response to global demands for socially and environmentally responsible practices. However, research on how these technologies work together is still limited. This study, using the Business Oriented Management Research Cycle (BOMRC), investigates how Big Data and Blockchain can synergistically promote transparent and sustainable business practices. The research includes a literature review, case study analysis, and application of findings to practical business scenarios. The findings reveal that Blockchain enhances transparency and traceability through a decentralized, secure system, while Big Data provides real time data processing to optimize operational efficiency and reduce environmental impact. By leveraging the synergy between these technologies, businesses can improve sustainability and maximize performance. Despite these advantages, high implementation costs and technological challenges remain significant barriers. This study contributes to understanding how Big Data and Blockchain can work together to promote sustainability, providing insights for overcoming barriers to their adoption. Future studies should explore sector specific applications to further advance these technologies.
Co-Authors Amroni, Amroni Darmawan, Afif Aditya Gates, John Doe HERNANI - Hussain, Nazim Liliasari , Muhammad Bachri Amran Yeanchon Henry Dulanlebit Yulianti Yulianti