DETEKSI KELAINAN OTAK HASIL MANETIC RESONANCE IMAGING (MRI) OTAK MENGGUNAKAN FIREFLY ALGORITHM PADA PELATIHAN JARINGAN RADIAL BASIS FUNCTION
Abstract: Radial Basis
Function Networks is one of the methods of artificial neural networks are used
to detect abnormalities in the brain
through images of magnetic resonance imaging (MRI) of the brain. MRI images of
the brain previously performed image processing to get the pattern for the introduction
of the network
training process. Image
processing used are
grayscale and histogram equalization.
The results of
image processing is
used as input
to the training
martiks networks using Radial
Basis Funtion Firefly
Algorithm. In the
training process to
obtain the optimal parameters for
validation test on the test data. Training data in this paper uses 20 brain MRI data
and the validation
test process using
brain MRI data
8. The results
of the training process with values obtained Means Square Error as 3,4734E-07,
and the optimal parameters can detect data on the validity of the validation
test with the percentage of 100%. Detection of brain abnormalities was
designed using the
Java programming language
NetBeans IDE 7.0.1
andMicrosoft Office 2003 to store data and image results.
Penulis: Nuri Fashichah, Auli
Damayanti, Herry Suprajitno
Kode Jurnal: jpmatematikadd130073
