Article Per Year (5 Year)
p-Index From 2020 - 2025
1.235
P-Index
This Author published in this journals
All Journal Nuansa Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA bit-Tech Jurnal Teknimedia: Teknologi Informasi dan Multimedia 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : bit-Tech

 1 
Klasifikasi Citra MRI Tumor Otak Menggunakan Metode Convolutional Neural Network Dede Husen
bit-Tech Vol. 7 No. 1 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i1.1576

Abstract

This study aims to improve the accuracy of brain tumor classification using Convolutional Neural Network (CNN) method on MRI images. In this study, various experiments were conducted using the original dataset and data that had undergone augmentation to increase the amount and variety of data. This study shows that data augmentation, such as flipping, scaling, and rotation, significantly improves model accuracy. The best model was obtained using flip and scale augmentation techniques with an average accuracy of 92.97%. These results show that the use of data augmentation techniques can improve the performance of CNN models in classifying brain tumors, and reduce the risk of overfitting. This research makes an important contribution to the field of medical diagnosis by providing a more accurate and efficient model for detecting brain tumors.
Co-Authors Daswa Daswa, Daswa Dede - Sandi Kusnawi Kusnawi Kusrini - - Kusrini Kusrini Nida Amalia Asikin Rio Andriyat Krisdiawan Sepriadi - Bumbungan