Article Per Year (5 Year)
p-Index From 2021 - 2026
1.881
P-Index
This Author published in this journals
All Journal Nuansa Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA bit-Tech Jurnal Abdi Insani Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat Bangsa JGGAG (Journal of Games, Game Art, and Gamification)
Dede Husen
Universitas Kuningan
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
EVALUASI TEKNIK AUGMENTASI DATA UNTUK KLASIFIKASI TUMOR OTAK MENGGUNAKAN CNN PADA CITRA MRI Dede Husen
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 5 No. 2 (2024): Desember 2024
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v5i2.220

Abstract

Brain tumor classification on Magnetic Resonance Imaging (MRI) scans poses a significant challenge in the fields of radiology and medical technology. To enhance diagnostic accuracy, Convolutional Neural Network (CNN) methods have shown great potential. However, the limitation of having an adequate training dataset remains a major obstacle in developing effective models. This study aims to evaluate the performance of CNN models by applying various data augmentation techniques for brain tumor classification and identifying the most effective augmentation techniques. The augmentation techniques tested include image scaling, random rotation, vertical and horizontal flipping, random brightness adjustments, and combinations of these various techniques. The results indicate that the scaling and vertical and horizontal flipping techniques yield the highest average accuracy of 92.97%, with a maximum accuracy of 100% achieved at the 20th epoch using the vertical and horizontal flipping technique. Thus, it is hoped that the findings of this study can be utilized by other researchers in selecting appropriate augmentation techniques for MRI images.
Co-Authors Daswa Daswa, Daswa Dede - Sandi Erik Kurniadi Fahmi Yusuf Kusnawi Kusnawi Kusrini - - Kusrini Kusrini Nida Amalia Asikin RIO ANDRIYAT KRISDIAWAN Sepriadi - Bumbungan Toni Khalimi Yulyanto Yulyanto