Open AccessClassification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm
Author(s) -
Daurat Sinaga,
Feri Agustina,
Noor Ageng Setiyanto,
Suprayogi Suprayogi,
Cahaya Jatmoko
Publication year - 2021
Publication title -
jais (journal of applied intelligent system)
Language(s) - English
Resource type - Journals
eISSN - 2503-0493
pISSN - 2502-9401
DOI - 10.33633/jais.v6i2.4627
Subject(s) - k nearest neighbors algorithm , pattern recognition (psychology) , artificial intelligence , co occurrence matrix , feature extraction , entropy (arrow of time) , homogeneity (statistics) , gray level , mathematics , grey level , computer science , algorithm , statistics , image (mathematics) , image processing , physics , image texture , quantum mechanics
Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.