IMPLEMENTASI AUGMENTASI DAN MODIFIKASI ARSITEKTUR DENSENET-DWS DALAM KLASIFIKASI PENYAKIT GLAUKOMA PADA CITRA RETINA

PRANATA, TEDDI and Desiani, Anita and Yahdin, Sugandi (2022) IMPLEMENTASI AUGMENTASI DAN MODIFIKASI ARSITEKTUR DENSENET-DWS DALAM KLASIFIKASI PENYAKIT GLAUKOMA PADA CITRA RETINA. Undergraduate thesis, Sriwijaya University.

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Abstract

Convolutional Neural Network (CNN) is a method that is often used in the process of image classification. CNN requires large and balanced data for each class in the training process, but the data used in this study is still relatively small and the number in each class is still not balanced, so we need a method to increase data, namely augmentation. DenseNet architecture is part of the CNN method that can be used to classify glaucoma disease, but DenseNet applies dense network exploitation that can increase the number of parameters, thereby reducing computational efficiency. Addition of depthwise separable convolution and dense layer at the end of the model will again increase the computational efficiency. The stages of the research carried out include data description, data augmentation, image repaired, training, testing, model evaluation, analysis and interpretation of results, and conclusion. The results of the evaluation of the model obtained are the values of accuracy, sensitivity, specificity, f1-score, and Cohens kappa respectively 90.32%, 85.63%, 92.82%, 85.45%, and 78.23%. Based on the results of the evaluation of the model, it was found that the augmentation and modification of the DenseNet architecture was able to classify the glaucoma disease in the image given well.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Glaukoma, DenseNet, Augmentasi
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Teddi Pranata
Date Deposited:	11 Aug 2022 06:53
Last Modified:	11 Aug 2022 06:53
URI:	http://repository.unsri.ac.id/id/eprint/76762

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