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All Journal Jurnal ULTIMA Computing Nusantara Hasana Journal TIERS Information Technology Journal Jurnal Kesehatan dan Kebidanan Nusantara (JKN)
Safrian Andromeda
Universitas Singaperbangsa Karawang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Medicine & Pharmacology 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.
MACHINE TO MACHINE (M2M) CONNECTIVITY BUSINESS FEASIBILITY ANALYSIS AND STRATEGY DEVELOPMENT CASE STUDY OF PT XYZ, A COMPANY IN INDONESIA, WITH SWOT ANALYSIS Safrian Andromeda; Bahar Amal; Ni Luh Bella Dwijaksara
Nusantara Hasana Journal Vol. 3 No. 8 (2023): Nusantara Hasana Journal, January 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v3i8.1099

Abstract

From 2016 to 2018, the number of new Very Small Aperture Terminal (VSAT) customers at PT XYZ declined. According to Safrian (2018), VSAT is included in quadrant two of the (Strength, Weakness, Opportunity, and Threat) SWOT diagram, so it requires diversification to create new opportunities and increase revenue. This research was conducted to analyze the feasibility of using M2M as a form of diversification. M2M was compared with VSAT using the Return on Investment (ROI) method and performance testing. Alternative M2M business strategies are also using the SWOT analysis. From the ROI results, M2M had 74%. M2M connectivity can provide more benefits with more efficient investment. From the performance test, M2M latency of 33ms is in the outstanding category, while VSAT of 596ms is in the poor category. The SWOT analysis found that the company entered quadrant one, with the strategy chosen being the SO strategy, which focuses on developing M2M technology.
Peran AI dalam Mengatasi Tantangan Diagnosis Dini Autisme: Solusi Teknologi dan Implikasinya Ni Luh Bella Dwijaksara; Safrian Andromeda
Jurnal Kesehatan dan Kebidanan Nusantara Vol. 2 No. 2 (2024): Jurnal Kesehatan dan Kebidanan Nusantara (JKN)
Publisher : CV. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69688/jkn.v2i2.110

Abstract

Diagnosis dini gangguan spektrum autisme (Autism Spectrum Disorder/ASD) merupakan langkah krusial dalam memastikan intervensi yang efektif dan meningkatkan kualitas hidup individu yang terdampak. Namun, proses ini kerap menghadapi berbagai tantangan, seperti keterbatasan akses ke tenaga profesional, waktu yang diperlukan untuk evaluasi menyeluruh, dan risiko kesalahan diagnosis akibat keterbatasan subjektivitas penilaian manusia. Penelitian ini bertujuan untuk mengatasi tantangan tersebut dengan mengeksplorasi peran kecerdasan buatan (Artificial Intelligence/AI) dalam mendukung diagnosis dini autisme. Metode yang digunakan dalam penelitian ini mencakup tinjauan literatur sistematis dan analisis studi kasus implementasi teknologi AI dalam diagnosis medis, khususnya pada autisme. Berbagai teknik AI, seperti pembelajaran mesin (machine learning), analisis video, dan pengolahan bahasa alami (natural language processing), diidentifikasi dan dievaluasi untuk menilai keefektifannya dalam mendeteksi gejala autisme sejak dini. Penelitian ini juga menggunakan pendekatan kualitatif melalui wawancara mendalam dengan ahli medis dan pengembang teknologi untuk memahami tantangan dan peluang integrasi AI dalam praktik diagnosis.Tujuan utama penelitian ini adalah untuk mengidentifikasi potensi dan keterbatasan AI dalam diagnosis dini autisme, serta menyusun rekomendasi strategis bagi pengembang teknologi dan tenaga medis dalam mengadopsi AI secara etis dan efektif. Hasil penelitian menunjukkan bahwa AI dapat secara signifikan meningkatkan akurasi dan efisiensi dalam diagnosis autisme, terutama dalam pengolahan data kompleks yang melibatkan pola perilaku dan interaksi sosial. Namun, penelitian ini juga mengungkapkan bahwa AI tidak dapat sepenuhnya menggantikan peran tenaga medis, karena masih terdapat risiko bias algoritma dan kebutuhan akan penilaian holistik dari seorang profesional. Selain itu, penggunaan AI dalam diagnosis memerlukan regulasi yang ketat serta pelatihan khusus bagi tenaga medis untuk memastikan teknologi ini digunakan dengan cara yang tepat dan bertanggung jawab. Penelitian ini menyimpulkan bahwa meskipun AI menawarkan solusi yang menjanjikan untuk tantangan diagnosis dini autisme, implementasinya harus dilakukan dengan pendekatan yang terukur dan berbasis bukti, dengan memperhatikan dampak sosial, etika, dan profesional.
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 Indri Purwita Sary Ni Luh Bella Dwijaksara Nurani Masyita Putri Alief Siswanto