Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma

YUNENDAH NUR FUADAH, IBNU DAWAN UBAIDULLAH, NUR IBRAHIM, FAUZI FRAHMA TALININGSING, NIDAAN KHOFIYA SY, MUHAMMAD ADNAN PRAMUDITHO

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ABSTRAK

Pada penelitian ini dilakukan perancangan arsitektur Convolutional Neural Network (CNN) yang terdiri dari 5 layer konvolusi dan 1-fully connected layer untuk mengklasifikasikan citra fundus kedalam kondisi normal, early, moderate, deep, dan ocular hypertension (OHT). Selanjutnya, model yang diusulkan dibandingkan dengan arsitektur AlexNet yang memiliki 5 layer konvolusi dan 3- fully connected layer. Data yang digunakan berupa citra fundus yang terdiri dari 3200 data latih, 800 data validasi, dan 1000 data uji. Optimasi model CNN dilakukan dengan melakukan pengujian hyperparameter yang terdiri dari learning rate, batch-size, epoch, dan optimizer. Selain itu, pada tahap training diimplementasikan 5-fold cross validation untuk seleksi model terbaik. Dengan model yang lebih sederhana dari AlexNet, model CNN usulan dapat memberikan performansi yang sama dengan arsitektur AlexNet yaitu akurasi 100%, presisi, recall, f1-score dan AUC score bernilai 1.

Kata kunci: glaukoma, citra fundus, convolutional neural network (CNN), AlexNet

 

ABSTRACT

This study proposes a Convolutional Neural Network with 5 convolutional layer and 1-fully connected layer to classify fundus images into normal, early, moderate, deep, and ocular hypertension (OHT) conditions. Furthermore, the proposed model is compared with the AlexNet architecture which has 5 convolution layers and 3- fully connected layers. The data used is a fundus image consisting of 3200 training data, 800 validation data, and 1000 test data. The optimization of the CNN model is performed by testing the hyperparameters consisting of learning rate, batch size, epoch, and optimizer. In addition, at the training stage, 5-fold cross validation is implemented to select the best model to be used in the test stage. With a simpler model from AlexNet, the proposed model provides 100% accuracy performance with precision values, recall, f1-score, and AUC score of 1.

Keywords: glaucoma, fundus images, convolutional neural network (CNN), AlexNet


Kata Kunci


glaukoma; citra fundus; convolutional neural network (CNN); AlexNet

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v10i3.728

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