Identifikasi Motif Batik Menggunakan Metode Glcm Dan Naive Bayes Classifier

Aprian, Rangga Akhir (2020) Identifikasi Motif Batik Menggunakan Metode Glcm Dan Naive Bayes Classifier. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

Identifikasi Motif Batik Menggunakan Metode GLCM Dan Naïve Bayes Classifier (Identification Batik Motive Using Method GLCM and Naïve Bayes Classifier). Aji Seto Arifianto S.ST, M.T, as a counselor Rangga Akhir Aprian Study Program of Informatics Engineering Majoring of Information Technology Program Studi Teknik Informatika Jurusan Teknologi Informasi ABSTRACT Batik is one of the traditional cultures that characterizes Indonesia in the eyes of the world. Batik was inaugurated by UNESCO as one of the world heritages on 2 October 2009. These different batik motive can be recognized by their different shapes and patterns. Visual observation can be represented by applications that apply computer vision. The method used for feature extraction is GLCM (Gray Level Co-Occurance Matrices) by taking 7 extraction features ASM, Entropy, Dissimilarty, Contrast, Correlation, Homogeneous, Autocorrelation and with 4 different angular directions, namely 0 °, 45 °, 90 ° and 135 °. Based on 28 features (7x4) the values ​​are classified using the Naive Bayes Classifier method. The data used are primary data from observations from the house of Batik Rolla Jember, Batik Abdu Ijen Bondowoso and Batik Sisik Melik Banyuwangi, each batik has 90 data, so the total dataset is 270 data, 255 training data used and as many testing data 15 data. The final results are tested using the Confusion Matrix test with the results of True Batik Jember 3 with False 2, True Batik Bondowoso 5 with false 0, True Batik Banyuwangi 4 with false 1. The system accuracy result is 80%. Key words : Computer vision, Batik, GLCM, Naïve Bayes Classifier

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