Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Aptisi Transactions on Technopreneurship (ATT) ADI Journal on Recent Innovation (AJRI)
Nanle, Zeze
Unknown Affiliation
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
IoT-Based Community Smart Health Service Model: Empowering Entrepreneurs in Health Innovation Jonas, Dendy; Purnomo, Hindriyanto Dwi; Iriani, Ade; Sembiring, Irwan; Kristiadi, Dedy Prasetya; Nanle, Zeze
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 1 (2025): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i1.461

Abstract

The Indonesian government aims to improve public health by integrating a unified health platform with regional systems for effective decision-making. However, the existing health information system is inadequate for broader decision-making needs, focusing primarily on individuals with existing health issues and not adequately addressing the needs of disaster victims, such as those affected by floods, accidents, and burns. Tangerang City, located in Banten Province, is a flood-prone area that faces annual disasters, highlighting this gap. To address this issue, this study proposes the development of a Health Internet of Things (HIoT) model designed to support rapid decision-making and enhance community health services. The proposed IoT-based network will be implemented in residential complexes, private clinics, schools, and places of worship, enabling real-time monitoring of health conditions and facilitating disaster or pandemic mitigation. Data collected from these communities will be transmitted to nearby hospitals for immediate medical assistance. Preliminary findings suggest that the IoT-based e-health system offers significant benefits, including faster patient care, improved data accuracy, and reduced operational costs. These results underscore the potential of HIoT to enhance community-based health services. The study provides a foundation for future research and practical applications. Further investigation will be conducted to evaluate the scalability of the system in diverse communities and its impact on long-term health outcomes.
Machine Learning Approaches for Cybersecurity in Distributed Cloud Infrastructures Prayitno, Dzovani Sandy Putra; Wibowo, Shesilia; Widjaya, Irene Apriani; Martono, Aris; Nanle, Zeze
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1417

Abstract

Rapid cloud adoption has transformed enterprise IT infrastructures, but also introduces complex cybersecurity challenges due to the distributed and dynamic nature of cloud environments, increasing exposure to sophisticated cyber threats. This study aims to design and evaluate machine learning-based approaches to enhance cybersecurity in distributed cloud infrastructures, focusing on improving threat detection accuracy, scalability, and operational efficiency in multi-cloud environments. The proposed method employs a layered machine learning framework integrating supervised and unsupervised algorithms to detect intrusions, anomalous behaviors, and policy violations across distributed cloud nodes, supported by real-time data collection and adaptive model training. A methodological illustration indicates that machine learning approaches can achieve higher detection accuracy approximately 90% compared to traditional rule based systems approximately 78%, while reducing false-positive rates from around 22% to 10%, and experimental results further confirm improved detection performance, reduced false positives, and faster response times while maintaining scalability under increasing workloads. These findings demon- strate that machine learning-driven cybersecurity solutions provide a more adaptive, scalable, and effective defense mechanism, supporting secure and sustainable digital transformation in modern cloud environments.
Co-Authors Ade Iriani Dedy Prasetya Kristiadi Hindriyanto Dwi Purnomo Irwan Sembiring Jonas, Dendy Martono, Aris Prayitno, Dzovani Sandy Putra Wibowo, Shesilia Widjaya, Irene Apriani