Rico Andrian, RA and Akmal Junaidi, Akmal and Meizano Ardhi Muhammad, MAM Butterfly identification using gray level co-occurrence matrix (glcm) extraction feature and k-nearest neighbor (knn) classification. Register: Jurnal Ilmiah Teknologi Sistem Informasi (Scientific Journals of Information System Technology), 6 (1). ISSN E-ISSN: 2502-3357

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Abstract

Gita Persada Butterfly Park is the only breeding of engineered in situ butterflies in Indonesia. It is located in Lampung and has approximately 211 species of breeding butterflies. Each species of Butterflies has a different texture on its wings. The Limited ability of the human eye to distinguishing typical textures on butterfly species is the reason for conducting a research on butterfly identification based on pattern recognition. The dataset consists of 600 images of butterfly’s upper wing from six species: Centhosia penthesilea, Papilio memnon, Papilio nephelus, Pachliopta aristolochiae, Papilio peranthus and Troides helena. The pre-processing stage is conducted using scaling, segmentation and grayscale methods. The GLCM method is used to recognize the characteristics of butterfly images using pixel distance and Angular direction 0o, 45o, 90o and 135o. The features used is angular second moment, contrast, homogeneity and correlation. KNN classification method in this study uses k values1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 based on the Rule of Thumb. The result of this study indicate that Centhosia penthesilea and Papilio nephelus classes can be classified properly compared to the other 4 classes and require a classification time of 2 seconds at each angular orientation. The highest accuracy is 91.1% with a value of in the angle of 90o and error rate8.9%. Classification error occured because the value of the test data features is more dominant with the value of the training image features in different classes than the supposed class. Another reason is because of imperfect test data.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer
Depositing User:	Rico Andrian
Date Deposited:	01 Dec 2020 06:36
Last Modified:	01 Dec 2020 06:36
URI:	http://repository.lppm.unila.ac.id/id/eprint/26940

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