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All Journal Cyberspace: Jurnal Pendidikan Teknologi Informasi Journal Medical Informatics Technology
Chandra Nugraha
Universitas Nusa Mandiri jakarta
Computer Science & IT Dentistry Education Engineering Medicine & Pharmacology Public Health Other

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IMPLEMENTATION OF THE DIJKSTRA ALGORITHM IN FINDING THE SHORTEST ROUTE TO AL-AZIZIYAH ISLAMIC BOARDING SCHOOL – LDII IN SAMARINDA Chandra Nugraha; Delvina tri agustin; arif Budiarto
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 6, No 1 (2022)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (576.704 KB) | DOI: 10.22373/cj.v6i1.10799

Abstract

Globalization Technology is growing very rapidly uncontrollably, the ease of use of technology is supported by the many emerging technology tools that are very easy to obtain, even some technology tools sold at very affordable prices. Android technology installed and packed in the form of smartphones is familiar to use even among people who are marginalized in the corners of the region. Not only are the intellect literate with technology utilizing the ease and development of this technology, even the lay people who utilize the internet network and are familiar with google can also mark the location of its place on google maps. With the globalization of this technology, the author will use the Implementation of Alghorithm Dijkstra In Finding the Shortest Route from the author's residence to Al-Aziziyah Islamic Boarding School – Ldii Di Samarinda. In this study, algorithm Dijkstra used alghorithm in determining the shortest path from the starting point to the destination point in a graff or shortest path with the help of LBS (Location Based Service), GPS, Google Maps, and Graff then found the shortest path was 4.88 km.
Glaucoma Detection in Fundus Eye Images using Convolutional Neural Network Method with Visual Geometric Group 16 and Residual Network 50 Architecture Chandra Nugraha; Sri Hadianti
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.7

Abstract

Glaucoma is an eye disease usually caused by abnormal eye pressure. One of the causes of abnormal eye pressure is blockage of fluid flow, which if detected too late can lead to blindness. Glaucoma can be identified by examining specific areas on the retina fundus image. The aim of this study is to detect positive and negative glaucoma in fundus images. The image data was obtained from the glaucoma_detection dataset, consisting of 520 images, including 134 glaucoma-infected images and 386 normal images. This study uses the Convolutional Neural Network (CNN) method with Visual Geometric Group-16 (VGG-16) and Residual Network-50 (ResNet-50) architectures. The research and testing results using the VGG-16 architecture obtained an accuracy rate of 78%, while using the ResNet-50 architecture obtained an accuracy rate of 80%.
Co-Authors arif Budiarto Delvina tri agustin Sri Hadianti