IZZUDDIN, ARIF (2022) KLASIFIKASI BATIK JAWA TIMUR MENGGUNAKAN BACKPROPAGASI DENGAN GRADIENT DESCENT ADAPTIVE LEARNING RATE WITH MOMENTUM. S1 thesis, UNIVERSITAS SULTAN AGENG TIRTAYASA.
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Abstract
Batik adalah salah satu warisan budaya leluhur bangsa Indonesia dan mempunyai banyak macam-macam motif. Batik memiliki berbagai macam motif yang sangat beragam. Batik telah diakui oleh UNESCO (United Nations Educational, Scientific and Cultural Organization) sebagai salah satu warisan budaya asli dari Indonesia, pada tanggal 2 Oktober 2009. Oleh karena itu, perlu adanya perhatian serius untuk menjaga batik. Penelitian ini dilakukan untuk mengklasifikasikan batik ke dalam kelasnya berdasarkan daerah asal batik sehingga mempermudah dalam pengenalan batik dan pemahaman tentang batik. Metode yang digunakan adalah gray level co�occurrence matrices (GLCM) untuk ekstraksi ciri tekstur, sedangkan untuk menentukan kedekatan antara citra uji dengan citra latih menggunakan metode backpropagasi adaptive learning rate with momentum (traingdx). Berdasarkan pengujian menggunakan 100 gambar uji menghasilkan akurasi 86% untuk 1 hidden layer dan 85% untuk 2 hidden layer
| Item Type: | Thesis (S1) | |||||||||
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| Additional Information: | Batik is one of the cultural heritages of the ancestor of Indonesia and has many kinds of motives. Batik has a variety of very diverse motifs. Batik has been recognized by UNESCO (United Nations Educational, Scientific and Cultural Organization) as one of the original cultural heritages of Indonesia, on October 2, 2009. Therefore, serious attention is needed to protect batik. This research was conducted to classify batik into classes based on the region of origin of batik so that it is easier in the introduction of batik and understanding of batik. The method used is the gray level co-occurrence matrices (GLCM) for texture feature extraction, while to determine the closeness between the test image and the training image using the adaptive learning rate backpropagasi method with momentum (traingdx). Based on testing using 100 test images produce accuracy 86% for 1 hidden layer and 85% for 2 hidden layer. Keyword : Batik, GLCM, traingdx, backpropagasi, accuracy | |||||||||
| Uncontrolled Keywords: | Kata Kunci : Kata kunci : Batik , GLCM, traingdx, backpropagasi, akurasi | |||||||||
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
| Divisions: | 03-Fakultas Teknik 03-Fakultas Teknik > 20201-Jurusan Teknik Elektro |
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| Depositing User: | Perpustakaan Pusat | |||||||||
| Date Deposited: | 22 Jul 2022 14:47 | |||||||||
| Last Modified: | 22 Jul 2022 14:47 | |||||||||
| URI: | http://eprints.untirta.ac.id/id/eprint/14711 |
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