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Journal : Scientific Journal of Informatics

Classification of Traditional Batik Motifs in Central Java using Gabor Filter andBackpropagationNeural Network Isnanto, R Rizal; Triwiyatno, Aris
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.26215

Abstract

Batik has a variety of varied motifs, each region in Indonesia has certain characteristics on batik motifs. Based on literature studies theuse of backpropagation neural network methods to recognize complex patterns has a satisfactory rate of success. The purpose of this research is to develop and apply neural networks that are fast, precise and accurate to classify batik designs and patterns. Types of batik motifs typical of Central Java that are used include; Truntum from Solo, Warak Ngendhog from Semarang, Sekar Jagad from Lasem, Burnt from Pati, and Jlamprang from Pekalongan. The image first undergoes RGB color feature extraction based on mean values of R, G, and B, and Gabor filter texture characteristics. The tests were carried out using 90 batik images, 60 batik images for training data and 30 batik images for testing data. The results of the study concluded that the best parameter settings were, the number of hidden layer 30 neurons in the first layer and 15 in the second layer, with 6 input layers and 5 output layers. Gabor filter with 90º orientation angle and wavelength 4 become the best combination in this study. From the results of training and testing results obtained an average accuracy of 93.3% in all batik classes in Central Java.
Classification of Traditional Batik Motifs in Central Java using Gabor Filter andBackpropagationNeural Network Isnanto, R Rizal; Triwiyatno, Aris
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.26215

Abstract

Batik has a variety of varied motifs, each region in Indonesia has certain characteristics on batik motifs. Based on literature studies theuse of backpropagation neural network methods to recognize complex patterns has a satisfactory rate of success. The purpose of this research is to develop and apply neural networks that are fast, precise and accurate to classify batik designs and patterns. Types of batik motifs typical of Central Java that are used include; Truntum from Solo, Warak Ngendhog from Semarang, Sekar Jagad from Lasem, Burnt from Pati, and Jlamprang from Pekalongan. The image first undergoes RGB color feature extraction based on mean values of R, G, and B, and Gabor filter texture characteristics. The tests were carried out using 90 batik images, 60 batik images for training data and 30 batik images for testing data. The results of the study concluded that the best parameter settings were, the number of hidden layer 30 neurons in the first layer and 15 in the second layer, with 6 input layers and 5 output layers. Gabor filter with 90º orientation angle and wavelength 4 become the best combination in this study. From the results of training and testing results obtained an average accuracy of 93.3% in all batik classes in Central Java.
Co-Authors Abdul Syakur Achmad Chaerodin Achmad Hidayatno Achmad Hidayatno Ade Riyantika Dewi Adhi Susanto Adi Mora Tunggul Adi Wijaya Adian Fatchur Rochim Adrian Putranda Rispurwadi Agus Suprihanto Agustini, Eka Puji Ahmad Ramdhani Ajub Ajulian Zahra Macrina Al Iman, Yusraka Dimas Alan Prasetyo Rantelino Albert Ginting An'im Almiktad Andhika Dewanta Andhika Hanifa Naufaliawan Andino Maseleno Anton Satria Prabuwono Ardianto Eskaprianda Ari Muhardono Arianto, Mufid Aris Puji Widodo Aris Sugiharto Aris Triwiyatno Astrid Aprillini Aulia Nastiti Aziz, RZ. Abdul Basuki Rahmat Masdi Siduppa Bhutra, Yuvraj Budi Warsito Chauhan, Rahul Damar Wicaksono Danang Respati Setyabudi Deddy Sucipta Syahril Dewi, Deshinta Arrova Dewi, Rany Puspita Dhody Kurniawan Dian Kurnia Widya Buana Dilan Arya Sujati Dimas Robby Firmanda Dini Indriyani Putri Donni Widagdo Dwi Novianto Eko Didik Widianto Eko Winarto Erizco Satya Wicaksono Ervin Adhi Cahyanugraha Fatima Setyani Ferry Dwi Setiyawan Firdaus Aditya GALIH WICAKSONO Gilang Aditya Pamungkas Handayani, Sri Hardiyanto Hardiyanto Hayu Andarwati Hefmi Fauzan Imron Hendy Cahya Lesmana Hilal Afrih Juhad Ike Pertiwi Windasari Imaduddin Amrullah Muslim Imam Tahyudin Irham Fa'idh Faiztyan Iwan Purwanto Jatmiko Endro Suseno Julianto, Dewa Rizki Rahmat Kataria, Yachi Kurniawan Teguh Martono Kurniawan, Tri Basuki Kusworo Adi Lia Lidya Roza Liga Filosa M Said Hasibuan Maizary, Ary Maman Somantri Martin Clinton Tosima Manullang Maulana Muhammad Iqbal Misik Puspajati Nurmadjid Saputri Mona Pradipta Hardiyanti Muh. Udka Muhamad Taopik Gibran Muhammad Fahmi Awaj Muhammad Kautsar Muhammad Nur Hadi Munawar Agus Riyadi Mustafa, Mustafa Mustafid Mustafid Nahdi Saubari Nanang Sulaksono, Nanang Nazwan, Nazwan Neneng Neneng Nugroho, Waluyo Nur Setyo Permatasasi Putri W Nurhayati, Oki Dwi Nurul Arifa Oky Dwi Nurhayati Pertiwi, Rahayu Putri Prakasa, Fawwaz Bimo Pramuko Tri Prastowo Prima Widyaningrum PUJI LESTARI Qoriani Widayati Rachel Chrysilla Tijono Refika Khoirunnisa Reza Najib Hidayat Rinta Kridalukmana Rinta Kridalukmana Rivaldi MHS Riyana Putri, Fayza Nayla Rizaldi Habibie Rizaldy Khair Rizky Gelar Maliq Rizky Parlika, Rizky Rosdelima Hutahaean Roza, Lia Lidya Rozak, Rofik Abdul Ruli Handrio Santoso, Imam Saptian, Fiega Adhi Sapto Nisworo Sasongko, Cornelius Damar Satya Arisena Hendrawan Setiawan, Annas Sri Lestari Sri Sumiyati Sri Widodo, Thomas Suhardjo Suhardjo Sumardi . Talitha Almira Taqwa Hariguna Teguh Dwi Prihartono Tikaningsih, Ades Toni Wijanarko Adi Putra Triloka, Joko Tyas Panorama Nan Cerah Ufan Alfianto Unaliya, Maitri Wandri Okki Saputra Wibaselppa, Anggawidia Widi Puji Atmojo Widiasmoro, Andi Wijaya, Elang Pramudya Yatim, Ardiyansyah Saad Yeh, Ming-Lang Yenita Dwi Setiyawati Yessy Kurniasari Yongki Yonatan Marbun Yudi Eko Windarto Yunus Anis, Yunus