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This Author published in this journals
All Journal IAIC Transactions on Sustainable Digital Innovation (ITSDI) International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Natasya Aprila Yusuf
University of Raharja
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology Other

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Enhancing Security and Privacy of Patient Data in Healthcare: A SmartPLS Analysis of Blockchain Technology Implementation Indri Handayani; Desy Apriani; Mulyati Mulyati; Achani Rahmania Az Zahra; Natasya Aprila Yusuf
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 5 No 1 (2023): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v5i1.603

Abstract

Security and privacy of patient data are critical concerns in the healthcare system. This research aims to investigate the impact of implementing blockchain technology on enhancing the security and privacy of patient data in healthcare. Using the SmartPLS analysis method, this study empirically examines the relationships between blockchain technology implementation and data security and data privacy within the healthcare system. The research sample consists of healthcare organizations that have implemented blockchain technology for data management. Data is collected through surveys and analyzed using SmartPLS to assess the effects of blockchain technology on data security and privacy. The findings reveal the positive influence of blockchain technology implementation on enhancing the security and privacy of patient data. The study also identifies challenges, such as scalability and interoperability, that need to be addressed for successful implementation. This research contributes to the existing literature by providing empirical evidence on the benefits and challenges of implementing blockchain technology to safeguard patient data in healthcare systems.
Assessing Customer Satisfaction in AI-Powered Services: An Empirical Study with SmartPLS Achani Rahmania Az Zahra; Dendy Jonas; Ita Erliyani; Rosdiana; Natasya Aprila Yusuf
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.432

Abstract

In the contemporary business landscape, the evaluation of customer satisfaction plays a pivotal role in assessing the effectiveness of AI-powered services. This empirical study, bolstered by the robust analytical tool, SmartPLS, systematically scrutinizes the intricate relationship between AI-powered services and customer satisfaction. With a rigorous and methodical service quality analysis, conducted with a sample of 189 respondents, we unveil the salient attributes and determinants that underpin customer satisfaction within the framework of AI-driven services. This research contributes substantially to a more profound comprehension of how organizations can strategically enhance customer satisfaction via the adept deployment of AI technologies. The ensuing findings, derived from the comprehensive analysis of 189 respondents, provide invaluable insights into the optimization of service quality within AI-powered ecosystems. These insights hold the potential to cultivate heightened levels of customer satisfaction and engender enduring loyalty, which is of paramount importance in the contemporary business landscape.
Evaluating the Effectiveness of Machine Learning in Cyber Threat Detection Aulia Khanza; Firdaus Dwi Yulian; Novita Khairunnisa; Natasya Aprila Yusuf; Asher Nuche
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ysdncf05

Abstract

In today's digital era, cyber threats pose significant challenges to organizations, necessitating more advanced detection methods. This study aims to evaluate the effectiveness of machine learning (ML) techniques in detecting cyber threats, focusing on supervised, unsupervised, and reinforcement learning models. Using datasets such as CICIDS2017, the study trains models including Random Forest, Support Vector Machines (SVM), and Neural Networks. The evaluation is based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that the Random Forest model outperforms others with an accuracy of 92.5\%, a precision of 91.8\%, and an F1-score of 92.4\%. This superior performance highlights its potential for real-time threat detection, as evidenced by a case study where the model effectively identified previously undetected cyber threats in a large technology company's network. However, the study also acknowledges challenges such as data quality and the need for continuous model updates. The findings suggest that integrating ML models into cybersecurity frameworks can significantly enhance threat detection efficiency. Future research should explore combining ML with traditional methods and improving model robustness against adversarial attacks to further advance cybersecurity measures.
Co-Authors Achani Rahmania Az Zahra Asher Nuche Aulia Khanza Dendy Jonas Desy Apriani Erliyani, Ita Firdaus Dwi Yulian Indri Handayani Mulyati Mulyati Novita Khairunnisa Rosdiana