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All Journal Infotech Journal Journal Medical Informatics Technology
Sabara, Edi
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Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

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SISTEM INFORMASI PEMBAYARAN SPP MENGGUNAKAN VIRTUAL ACCOUNT BERBASIS WEBSITE PADA SEKOLAH IMAM MUSLIM BEKASI Sabara, Edi; Heriyanto
INFOTECH journal Vol. 9 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i1.4648

Abstract

Currently, the Imam Muslim School located on Jalan Raya Kranggan Gg Elang IX, Rt 002/Rw 003 Jatiraden, Jatisampurna, Bekasi, is still using manual bookkeeping and payment systems. With payment using a manual system this can make it difficult for students who want to make tuition payments, therefore the author is trying to create an Educational Contribution Payment Information System (SPP) program using a website-based virtual account using the PHP and MySQL programming languages. This system can simplify the process of operational activities in schools in managing tuition payments, reporting and also make it easier for students to make payments. Because payments can be made online through bank channels or modern payment channels such as Indomaret and Alfamart
Optimising Cataract Detection in Fundus Images through EfficientNet-Based Classification Ibrahim, Andi; Sabara, Edi; Dirsam, Winarlin; Aziz, Faruq
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.25

Abstract

Turbidity of the lens of the eyeball that causes blindness or loss of vision is known as a cataract. By diagnosing the causes and symptoms of cataracts, early detection helps patients in prevention and treatment. The purpose of the research was to classify the image of the fundus into two classes: normal and cataract. The study also looked at how the optimizers for stochastic gradient descent, adaptive moment estimation, root mean square propagation, adaptive gradient algorithm, adaptive delta, and Nesterov-accelerated adaptive moment estimation stacked up against each other. We used the EfficientNet architecture in CNN and preprocessed the normal fundus and cataract fundus images by dividing each into training data (N = 80) and validation data (N = 20) from the Kaggle repository. We added test data from the normal fondus image (N =20) to see the accuracy of the results. We get 100% accuracy of training data, 87% and 77% validation data, and 100% and 95% test data.
Co-Authors Andi Ibrahim Aziz, Faruq Dirsam, Winarlin Heriyanto