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All Journal Techno.Com: Jurnal Teknologi Informasi International Journal of Artificial Intelligence Research Technomedia Journal Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) ProBisnis : Jurnal Manajemen International Journal of Cyber and IT Service Management (IJCITSM) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Adam Faturahman
Universitas Raharja
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : journal of computer science and technology application

 1 
AI-Driven Big Data Solutions for Personalized Healthcare: Analyzing Patient Data to Improve Treatment Outcomes Ageng Setiani Rafika; Adam Faturahman; Bintang Nandana Henry; Firdaus Dwi Yulian; Mohammed Hassan
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.61

Abstract

The advent of AI-driven big data solutions has transformed personalized healthcare by enabling the analysis of vast and complex patient datasets to optimize treatment outcomes. This study aims to evaluate the effectiveness of AI models in improving healthcare delivery through enhanced diagnostic accuracy, reduced processing times, and personalized treatment plans. The research utilizes AI models to process extensive patient data from electronic health records, wearable devices, and genetic information. The results show an impressive accuracy rate of 93%, a 25% reduction in diagnostic errors, and significant improvements in patient outcomes, including 72% of patients receiving more accurate diagnoses and 65% experiencing faster recovery. A comparison with traditional methods highlights the advantages of AI in scalability, efficiency, and reliability, offering a clear improvement over existing healthcare approaches. However, challenges such as data bias, ethical concerns, and scalability need to be addressed to en- sure the responsible application of AI in healthcare systems. In conclusion, this research provides valuable insights for healthcare organizations that aim to implement AI-driven solutions, fostering the advancement of patient care and encouraging innovation in the industry. The findings suggest that AI-powered big data solutions have the potential to revolutionize healthcare, improving diagnostic precision and treatment personalization, ultimately enhancing patient satisfaction and outcomes.
Big Data Governance Framework for Trustworthy Artificial Intelligence Decision Systems Adam Faturahman; Alfri Adiwijaya; Ardivan Avandi; Nanda Septiani; Kristina Vaher
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kp2fkq63

Abstract

The rapid adoption of Artificial Intelligence (AI) decision systems has increased organizational dependence on large-scale data, making big data governance a critical requirement for ensuring reliable and responsible decision-making. Although AI systems are often evaluated based on predictive accuracy and computational performance, their trustworthiness is strongly influenced by the quality, security, privacy, traceability, and fairness of the data used throughout the AI lifecycle. This study aims to develop a Big Data Governance Framework for Trustworthy AI Decision Systems by integrating key governance dimensions with trustworthy AI requirements. A qualitative conceptual framework development approach was employed, supported by structured literature review, thematic synthesis, and design science research principles. Relevant literature on big data governance, trustworthy AI, data quality, privacy, security, explainability, accountability, fairness, and AI decision systems was reviewed to identify recurring concepts and research gaps. The results show that trustworthy AI decision systems require seven core governance dimensions: data quality governance, security and privacy governance, metadata and data lineage, bias and fairness control, explainability support, accountability mechanisms, and continuous monitoring. These dimensions strengthen trustworthy AI capabilities, including reliability, transparency, explainability, fairness, privacy preservation, security, robustness, and auditability. The proposed framework demonstrates that trustworthy AI is not only determined by algorithmic performance but also by strong data governance across the AI lifecycle. This study concludes that effective big data governance can improve decision accuracy, traceability, accountability, risk reduction, and stakeholder trust in AI-based decision systems.
Co-Authors Abdullah Arif Kamal Alfiah Khoirunisa Alfri Adiwijaya April Lansonia Ardivan Avandi Bintang Nandana Henry Chandra Lukita Dede Cahyadi Dhiyah Ayu Rini Kusumawardhani Ellen Dolan Erick Febriyanto Firdaus Dwi Yulian Fitra Putri Oganda Harahap, Eka Purnama Hendriyanti Haryani Jett Lee Wilson Kenita Zelina Kristina Vaher Leli Nirmalasari Mitra Trima Dessincer Putri Mochamad Heru Riza Chakim Mohammed Hassan Muhamad Yusup Nanda Septiani Ninda Lutfiani Nugroho Prihantoni Wibowo Nuke Puji Lestari Santoso Qurotul Aini Rafika, Ageng Setiani Rakhmansyah, Mohamad Refa Azka Reyhan Algiffary Gunawan Sandy Kosasi Sella Avionita Shofiyul millah Sri Lestari Pujiastuti, Sri Lestari Steven Harazaki Lase Sudaryono Sudaryono Sulis Setiawati Syifa Muthiatunnisa Tuti Nurhaeni Tuti Nurhaeni Untung Rahardja