CLASSIFICATION OF CAT BREED TYPE IMAGES USING GLCM AND DECISION TREE METHODS

Putra, Dimas Aditya (2026) CLASSIFICATION OF CAT BREED TYPE IMAGES USING GLCM AND DECISION TREE METHODS. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

This study aims to develop a classification system for cat breed images using the Gray Level Co-occurrence Matrix (GLCM) method as a texture feature extraction technique and the Decision Tree algorithm as a classification method. The dataset used consists of a number of cat imagery from several breeds that have gone through the preprocessing stage, such as resizing and image conversion to grayscale. The texture features produced from GLCM include contrast, correlation, energy, and homogeneity, which are then used as inputs in the classification process using Decision Tree. The built model is evaluated using testing methods such as accuracy, precision, and recall to measure system performance. The results of the study show that the combination of the GLCM and Decision Tree methods is able to provide a fairly good classification performance in distinguishing cat breeds based on imagery. The system is expected to help in the process of automatic identification of cat breeds and can be further developed with more complex methods to improve accuracy.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Anggraeny, Fetty Tri	NIDN0711028201	fettyanggraeny.if@upnjatim.ac.id
Thesis advisor	Putra, Chrystia Aji	NIDN0008108605	ajiputra.if@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Mr Dimas Aditya Putra

Date Deposited:

22 Jul 2026 07:26

Last Modified:

22 Jul 2026 07:26

URI:

https://repository.upnjatim.ac.id/id/eprint/57746