Article Per Year (5 Year)
p-Index From 2020 - 2025
3.346
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electronics and Instrumentation Systems Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Proceedings of KNASTIK Jurnal sistem informasi, Teknologi informasi dan komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Bulletin of Computer Science Research Jurnal Pengabdian kepada Masyarakat Bina Darma Algoritme Jurnal Mahasiswa Teknik Informatika Arcitech: Journal of Computer Science and Artificial Intelligence MDP Student Conference
Wijang Widhiarso
Universitas Multi Data Palembang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Indonesian Journal of Electronics and Instrumentation Systems

 1 
Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network Wijang Widhiarso; Yohannes Yohannes; Cendy Prakarsah
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 2 (2018): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (792.128 KB) | DOI: 10.22146/ijeis.34713

Abstract

Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.
Co-Authors Ahmad Farisi Andres, Yohanes Asyraq, Cerwyn Cendy Prakarsah Chandra Wijaya Dafid Dafid Derry Alamsyah Edi Winarko Edo Renaldo Ericco Andreas Ervina Patricia Arifin Hafiz Irsyad Kersen Kersen Khan, Zicola Vladimir VIky Kurniawan, Wiji Manalu, Josua Michael Michael Muhammad Irfan Muhammad Rachmadi, Muhammad Muhammad Rizky Pribadi Purwasih, Opita Ricky Putra Sardika Sardika, Ricky Putra SATRIAWAN, MUHAMMAD AKBAR Sri Hartati Usman Usman Vera Nofita Verren Oktaviana Wiwik Handayani Yuda, Andika Dirma