Ekstraksi Fitur Tekstur dan Warna pada Kulit Katak Menggunakan GLCM dan Momen Warna
DOI:
https://doi.org/10.31961/eltikom.v6i1.287Keywords:
Anura, Gray Level Co-occurrence Matrix, Momen Warna, KStarAbstract
Anura is an order in the Amphibian class consisting of frogs and toads. Anura is very important in the ecosystem, especially its role as part of the food chain. Anura's main role is to maintain the balance of the ecosystem and as a bioindicator agent for changing environmental conditions such as water pollution, habitat destruction, disease and parasites, and climate change. This research applies digital image processing technology which is expected to assist in detecting types of frogs based on color and texture. This research uses 5 types of frogs, namely kongkang gading, kongkang poison, striped trees, small trees and flying trees with 20 images of each type of frog. This research uses the color feature extraction methods such Color Moment and texture extraction GLCM (Gray Level Co-occurance Matrix), then classified using K-Star. The results of the K-Star performance evaluation to classify the 5 types of frogs obtained the Accuracy (Acc) value of 0.93, Precision (Prec) of 0.94, Recall (Rec) of 0.93 and F-measure of 0.93. So that the classification results of frog species on texture and color feature extraction using the GLCM method and the Color Moment with the K-Star classification method have high performance and can work well.
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B. O. Hua, M. A. Fu-Long, dan J. Li-Cheng, “Research on computation of GLCM of image texture,” Acta Electron. Sin., vol. 1, no. 1, hal. 155–158, 2006.
A. Materka, M. Strzelecki, dan others, “Texture analysis methods--a review,” Tech. Univ. lodz, Inst. Electron. COST B11 report, Brussels, vol. 10, no. 1.97, hal. 4968, 1998.
A. Prahara dan A. Azhari, “Analisis Fitur Warna dan Tekstur untuk Metode Deteksi Jalan,” J. Ilmu Tek. Elektro Komput. dan Inform., vol. 2, no. 2, hal. 77, 2016.
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C. Neelavathi dan S. M. Jagatheesan, “Improving Spam Mail Filtering Using Classification Algorithms with Partition Membership Filter,” Int. Res. J. Eng. Technol., vol. 3, no. 1, hal. 380–383, 2016.
I. P. G. S. Pradnyana, L. Novamizanti, dan H. Fauzi, “Perancangan Sistem Pendeteksi Genangan Air Potensi Perkembangbiakan Nyamuk Melalui Foto Citra Udara Dengan Metode Gray Level Co-occurrence Matrix (glcm),” eProceedings Eng., vol. 2, no. 2, 2015.
R. M. Haralick, K. Shanmugam, dan I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man. Cybern., no. 6, hal. 610–621, 1973.
D. Hardiyanto, S. Kristiyana, D. Kurniawan, dan D. A. Sartika, “Klasifikasi Motif Citra Batik Yogyakarta Menggunakan Metode Adaptive Neuro Fuzzy Inference System,” Setrum Sist. Kendali-Tenaga-elektronika-telekomunikasi-komputer, vol. 8, no. 2, hal. 229–237, 2019.
B. S. Anami dan V. C. Burkpalli, “Texture based identification and classification of bulk sugary food objects,” ICGST-GVIP J., vol. 9, no. 4, hal. 9–14, 2009.
P. Mohanaiah, P. Sathyanarayana, dan L. GuruKumar, “Image texture feature extraction using GLCM approach,” Int. J. Sci. Res. Publ., vol. 3, no. 5, hal. 1, 2013.
M. Sharma, “Artificial neural network fuzzy inference system (ANFIS) for brain tumor detection,” arXiv Prepr. arXiv1212.0059, 2012.
I. Santoso, Y. Christyono, dan M. Indriani, “Kinerja Pengenalan Citra Tekstur menggunakan Analisis Tekstur Metode Run Length,” in Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 2007.
D. Putra, Pengolahan citra digital. Penerbit Andi, 2010.
T. Kavzoglu dan I. Colkesen, “Entropic distance based K-Star algorithm for remote sensing image classification,” Fresenius Environ. Bull., vol. 20, no. 5, hal. 1200–1207, 2011.
D. Y. Mahmood dan M. A. Hussein, “Intrusion detection system based on K-star classifier and feature set reduction,” Int. Organ. Sci. Res. J. Comput. Eng. Vol, vol. 15, no. 5, hal. 107–112, 2013.
O. Rösler dan D. Suendermann, “A first step towards eye state prediction using eeg,” Proc. of the AIHLS, 2013.
J. G. Cleary dan L. E. Trigg, “K*: An instance-based learner using an entropic distance measure,” in Machine Learning Proceedings 1995, Elsevier, 1995, hal. 108–114.
D. Chicco dan G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,” BMC Genomics, vol. 21, no. 1, hal. 1–13, 2020.
T. Sutojo, P. S. Tirajani, C. A. Sari, E. H. Rachmawanto, dan others, “CBIR for classification of cow types using GLCM and color features extraction,” in 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2017, hal. 182–187.
A. Tharwat, “Classification assessment methods,” Appl. Comput. Informatics, 2020.
R. Puri dan K. Khamrui, “Application of Quantitative Descriptive Analysis (QDA), Principal Component Analysis (PCA) and Response Surface Methodology (RSM) in standardization of cham-cham making,” 2015.
T. Hastie, S. Rosset, J. Zhu, dan H. Zou, “Multi-class adaboost,” Stat. Interface, vol. 2, no. 3, hal. 349–360, 2009.
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Published
15-05-2022
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[1]
Kurniasari, A. et al. 2022. Ekstraksi Fitur Tekstur dan Warna pada Kulit Katak Menggunakan GLCM dan Momen Warna. Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer. 6, 1 (May 2022), 1–12. DOI:https://doi.org/10.31961/eltikom.v6i1.287.
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