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All Journal Jurnal ULTIMA Computing Nusantara Hasana Journal TIERS Information Technology Journal
Safrian Andromeda
Universitas Singaperbangsa Karawang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Nursing

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Performance Comparison of YOLOv5 and YOLOv8 Architectures in Human Detection using Aerial Images Indri Purwita Sary; Safrian Andromeda; Edmund Ucok Armin
Ultima Computing : Jurnal Sistem Komputer Vol 15 No 1 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i1.3204

Abstract

The development of UAV technology has reached the stage of implementing artificial intelligence, control, and sensing. Cameras as UAV data inputs are employed to ensure flight safety, search for missing persons, and disaster evacuation. Human detection using cameras while flying is the focus of this article. The application of human detection in pedestrian areas using aerial image data is used as the dataset in the deep learning input process. The architectures discussed in this study are YOLOv5 and YOLOv8. The precision, recall, and F1-score values are used as comparisons to evaluate the performance of these architectures. When both architecture performances are applied, YOLOv8 outperforms YOLOv5. The achieved performance of YOLOv8 is a precision of 84.62%, recall of 75.93%, and F1-score of 79.98%.
LEVERAGING CLOUD COMPUTING FOR TELEMEDICINE: ADVANCES IN MEDICAL IMAGE COMPRESSION, SECURITY, AND SAFETY Ni Luh Bella Dwijaksara; Safrian Andromeda; Putri Alief Siswanto; Agrippina Waya Rahmaning Gusti; Bahar Amal; Nurani Masyita
Nusantara Hasana Journal Vol. 3 No. 4 (2023): Nusantara Hasana Journal, September 2023
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v3i4.978

Abstract

Telemedicine has revolutionized healthcare delivery by providing remote medical consultations. This study explores the role of cloud computing in enabling telemedicine, with a particular focus on the utilization of medical image compression techniques and ensuring robust security and safety measures. The integration of cloud computing with telemedicine offers numerous advantages including scalable storage, flexible computing resources, and improved accessibility to medical data and applications. One critical aspect of telemedicine is the transmission and analysis of medical images such as X-rays, CT scans, and MRIs. However, the large size of these images can pose challenges in terms of the transmission speed and storage capacity. To address this, medical image are employed to reduce the size of images without a significant loss of diagnostic information. Security and safety are paramount in telemedicine systems, particularly when dealing with sensitive patient data and medical images. Cloud computing provides a robust infrastructure for ensuring data security and privacy, enabling the secure transmission and storage of medical images. This abstract discusses the implementation of encryption, access-control mechanisms, and authentication protocols to safeguard patient data during transmission and storage in the cloud. By leveraging cloud computing technologies, telemedicine can overcome geographical barriers and enhance healthcare accessibility for patients and healthcare professionals. Exploration of these topics will contribute to improving the efficiency, reliability, and quality of telemedicine services, ultimately leading to better patient outcomes and increased healthcare accessibility in both urban and rural settings.
AI in Dermatology: A Systematic Review on Skin Cancer Detection Safrian Andromeda; Ni Luh Bella Dwijaksara
TIERS Information Technology Journal Vol. 5 No. 1 (2024)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v5i1.5444

Abstract

Skin cancer is the most common type of cancer worldwide and poses a significant public health challenge. Its visible nature often leads individuals to seek medical attention, highlighting the importance of early detection for better patient outcomes. In recent years, Artificial Intelligence (AI) has shown promise in improving the detection and diagnosis of skin cancer, offering the potential to enhance clinical outcomes. A systematic review was conducted, involving a comprehensive literature search to identify studies focused on AI techniques in detecting, diagnosing, or treating skin cancer. Strict inclusion and exclusion criteria were applied to assess the eligibility of scientific articles, resulting in the selection of nine relevant studies. These studies were analyzed to address predefined research questions about the effectiveness of AI in diagnosing skin cancer. The review found that AI-assisted clinicians achieved higher sensitivity and specificity in diagnosing skin cancer than those without assistance. Various AI algorithms demonstrated high sensitivity in detecting skin cancers, highlighting their potential to support primary care clinicians in evaluating suspicious lesions. The analysis also highlighted the effectiveness of smartphone applications designed for skin cancer risk assessment, which could facilitate self-examinations and enhance early detection rates. Despite these promising findings, the field of AI in skin cancer diagnosis is still in its early stages. Challenges remain, including developing robust algorithms, addressing data quality issues, and improving the interpretability of AI-generated results. Collaboration between AI developers and healthcare professionals is crucial to ensure these tools' clinical effectiveness and safety. The review emphasizes the need for continued validation of AI technologies and their integration into clinical practice to improve patient outcomes and alleviate the burden on healthcare systems.
Co-Authors Agrippina Waya Rahmaning Gusti Bahar Amal Edmund Ucok Armin Ni Luh Bella Dwijaksara Nurani Masyita Putri Alief Siswanto Sary, Indri Purwita