Identifikasi Jenis Ikan Arwana Menggunakan Metode SVM Dengan Kombinasi Ekstraksi Fitur HSV Dan GLCM

Sutrisni, Erica Aprilia (2025) Identifikasi Jenis Ikan Arwana Menggunakan Metode SVM Dengan Kombinasi Ekstraksi Fitur HSV Dan GLCM. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

This research aims to develop an automatic classification system to identify types of arowana fish based on digital images using the Support Vector Machine (SVM) method combined with HSV and Gray Level Co-occurrence Matrix (GLCM) feature extraction. Arowana fish have high economic value and various types, making accurate identification important for collectors, sellers, and buyers. The stages of this research include image preprocessing such as resizing, segmentation using U²-Net, grayscale conversion, and data augmentation. Subsequently, color features are extracted using the HSV color space, and texture features are extracted using the GLCM method. These features are then used as input for the SVM model during the training and testing phases. The resulting classification outputs the type of arowana fish along with recommended size and price range information. Model evaluation shows that the combination of HSV and GLCM features performs well in distinguishing arowana types, achieving a classification accuracy of 96%. As the final stage, the system is implemented as a web application using the Flask framework, allowing users to upload fish images directly and receive classification results along with recommendations in real time through an interactive web interface.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Sari, Anggraini Puspita	0716088605	anggraini.puspita.if@upnjatim.ac.id
Thesis advisor	Maulana, Hendra	1423128301	hendra.maulana.if@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)
T Technology > T Technology (General) > T385 Computer Graphics

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Erica Aprilia Sutrisni

Date Deposited:

20 Jun 2025 01:21

Last Modified:

20 Jun 2025 01:21

URI:

https://repository.upnjatim.ac.id/id/eprint/38539