Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Motif Batik
DOI:
https://doi.org/10.29407/inotek.v4i2.131Abstract
Batik merupakan salah satu warisan kesenian dari leluhur yang ditetapkan oleh badan dunia PBB yakni UNESCO sebagai warisan budaya bangsa Indonesia. Pengetahuan tentang macam macam motif batik secara eksplisit yang jarang dimiliki oleh orang awam dan hanya dimiliki orang-orang tertentu yang memiliki keahlian pada bidang tersebut. Untuk mengatasi permasalahan tersebut, perlu dibuat sistem pengenalan jenis-jenis batik yang optimal melalui pendekatan algoritma Convolution Neural Network yang diharapkan dapat memberikan pengenalan motif batik secara optimal. Pengujian dilakukan dengan melakukan pengujian terhadap vektor yang ada. Berdasarkan nilai probabilitas yang diperoleh dari setiap pengujian, maka membuktikan bahwa metode ini mampu mengklasifikasi batik dengan cukup baik.
Keywords:
Convolutional Neural Network, Batik, Klasifikasi##plugins.themes.default.displayStats.downloads##
References
Adam. R., R. 2016. Deep Learning Untuk Pengenalan Pelafalan Huruf Hijaiyah Berharakat. UGM: Program Studi Ilmu Komputer. Skripsi.
Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K. 2006. Extreme learning machine : theory and applications. Int. J. of Neurocomputing 70(2006): 489-501.
Krizhevsky, A., Sutskever, I. dan Hinton, G.E. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems, pp.1–9.
Sarraf, S & Tofighi, G. 2016. Classification of Alzheimer’s Disease using fMRI Data and Deep Learning Convolutional Neural Networks. arXiv:1603.08631.
Wicaksono, Ardian Yusuf., Suciati, Nanik., Fatichah, Chastine., Uchimura, Keiichi. dan Koutaki, Gou. 2017. Modified Convolutional Neural Network Architecture for Batik Motif Image Classification. IPTEK, Journal of Science Vol. 2 No. 1.
Suartika, I. W. E. P., Wijaya, A. R & Soelaiman, R. 2016. Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101. Jurnal Teknik ITS Vol. 5 No. 1.
Sutoyo, T. 2009. Teori Pengolahan Citra Digital. Yogyakarta:Andi.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Atho’ul Muwafiq, Danar Putra Pamungkas
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
