Klasifikasi Penyakit Mata Menggunakan Ensemble Classifier CNN-KNN-SVM Berdasarkan Hasil Ekstraksi Fitur CNN

Wardhani, Adil Sandy (2024) Klasifikasi Penyakit Mata Menggunakan Ensemble Classifier CNN-KNN-SVM Berdasarkan Hasil Ekstraksi Fitur CNN. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

The rate of eye diseases in Indonesia has increased significantly in recent years. In fact, there are several different types of eye diseases and it is quite difficult to determine the type of eye disease suffered. If eye disease is not quickly detected or classified, it can cause a decrease in eye function and even blindness. In classifying eye disease types, machine learning algorithms can be used. Convolutional Neural Network (CNN) algorithm is one of the most common machine learning algorithms in image classification and is often used in many studies to process visual data. Besides CNN, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms can also be used for image classification purposes. This research aims to develop an eye disease classification system using an ensemble of CNN, KNN and SVM classifiers based on the results of CNN feature extraction. From the ensemble classifier, majority voting will be carried out to take classification results. The results obtained from the research are the classification accuracy rate using the CNN, KNN and SVM classifier ensemble method by getting the highest result reaching 93.174%.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Anggraeny, Fetty Tri	NIDN0711028201	fettyanggraeny.if@upnjatim.ac.id
Thesis advisor	Rizki, Agung Mustika	NIDN0025079302	agung.mustika.if@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

adil sandy wardhani

Date Deposited:

04 Jun 2024 04:19

Last Modified:

04 Jun 2024 04:19

URI:

https://repository.upnjatim.ac.id/id/eprint/23598