Implementasi Jaringan Syaraf Tiruan Menggunakan Metode Self-Organizing Map Pada Klasifikasi Citra Jenis Ikan Kakap

Abstract

Snapper is one of the favorite fish for consumption because it has a myriad of benefits for the human body. There are many types of snapper, especially snapper which is often found in Indonesian waters. Knowing the types of snapper is important knowledge because snapper has different characteristics, for example there are snapper that can be consumed and there are also types of snapper that can be cultivated. However, the lack of information and similar types of snapper makes it difficult for people to identify the type of snapper. This study aims to implement a Self-Organizing Map (SOM) artificial neural network for classification of snapper species based on color and texture characteristics. In order to provide information about the snapper object to be classified, color and texture feature extraction is used. In color feature extraction, RGB and HSV parameters are used and for texture features, the Gray Level Co-occurrence Matrix (GLCM) approach is applied. Furthermore, the characteristic results obtained will be classified using the Self-Organizing Map (SOM) algorithm which divides the input patterns into certain classes so that the network output is in the form of classes that have similarities to the given input. Based on the results of the accuracy test, the built model is capable of producing an accuracy of 89.89%. Thus, the SOM model built for image classification of snapper species is in the good category.