Deteksi dan Pengenalan Jenis Corak Batik Nusantara Menggunakan Metode CNN Berbasis Android
Kata Kunci:
Batik, Detection, CNN, AndroidAbstrak
This research aims to develop a system for the detection and recognition of traditional Indonesian batik patterns using Convolutional Neural Network (CNN) method based on the Android platform, utilizing TensorFlow Lite as the framework. The research is motivated by the importance of preserving and promoting the cultural heritage of batik patterns, which represent the distinctive and treasured cultural identity of Indonesia. The methodology involves training a CNN model using a dataset consisting of 500 images of various batik patterns from 10 different types of batik. The dataset is divided into training and testing data in an 80:20 ratio. The results of the research indicate that the developed model for the detection and classification of batik patterns achieves high accuracy, with a training data accuracy of 92.25% and a testing data accuracy of 94%. In conclusion, the research demonstrates that the developed model is capable of accurately recognizing and detecting various traditional batik patterns. The research has practical benefits for the public, as it enhances knowledge and understanding of different batik patterns. Furthermore, the research contributes to the advancement of knowledge and the researcher's proficiency in implementing the CNN method for real-time detection of batik patterns.Referensi
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016, November). Tensorflow: a system for large-scale machine learning. In Osdi (Vol. 16, No. 2016, pp. 265-283).
Agustin, A. (2014). Sejarah batik dan motif batik di Indonesia. In Seminar Nasional RisetInovatif II (No. 2339-1553, p. 541).
Alfikri, R. H., Utomo, M. S., Februariyanti, H., & Nurwahyudi, E. (2022). Pembangunan Aplikasi Penerjemah Bahasa Isyarat Dengan Metode Cnn Berbasis Android. Jurnal Teknoinfo, 16(2), 183-197.
Arifianto, J. (2022). Aplikasi Web Pendeteksi Jerawat Pada Wajah Menggunakan Model Deep Learning Dengan Tensorflow.
Bariyah, T., Rasyidi, M. A., & Ngatini, N. (2021). Convolutional Neural Network untuk metode klasifikasi multi-label pada motif batik. Techno. Com, 20(1), 155-165.
Gonzalez, R.C., Woods, R.E., dan Eddins, S.L. (2004). Digital Image Processing Using MATLAB. Pearson Education.
Khan, S., H. Rahmani, S. Shah, and D.M Bennamoun. (2018). A Guide to Convolutional Neural Networks for Computer Vision. New York: Morgan & Claypool Publishers.
Lestari, S. D. (2012). Mengenal Aneka Batik. PT Balai Pustaka (Persero).
Maiyana, E. (2018). Pemanfaatan android dalam perancangan aplikasi kumpulan doa. Jurnal Sains dan Informatika: Research of Science and Informatic, 4(1), 54-65.
Putri, R. A., & Rochmawati, N. (2019). Penerapan Algoritma Support Vector Machine untuk Klasifikasi Motif Citra Batik Solo Berdasarkan Fitur Multi-Autoencoders. Journal of Informatics and Computer Science (JINACS), 1(01), 56-63
Saefurrohman, S., & Ningsih, D. H. U. (2016). Desain Motif Batik Dengan Metode Fraktal Dan Algoritma L-System untuk Membangun Pustaka Batik Wali. Dinamik, 21(1), 42-51.
Savitri, A. (2019). Revolusi industri 4.0: mengubah tantangan menjadi peluang di era disrupsi 4.0. Penerbit Genesis.
Shafira, T. (2018). Implementasi Convolutional Neural Networks Untuk Klasifikasi Citra Tomat Menggunakan Keras (Doctoral dissertation, Universitas Islam Indonesia).
Zindani, A. Y., Amalia, A., & Putro, F. W. (2020). Pendeteksi Kendaraan Untuk Keamanan Perlintasan Kereta Api. Lomba Karya Tulis Ilmiah, 1(1), 35-47.
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2023 Faiq Fahrian Khoirul Anam Al Aziz, Saefurrohman
Artikel ini berlisensi Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant 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 acknowledgement 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 acknowledgement 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 (See The Effect of Open Access).
Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.
In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License
YOU ARE FREE TO:
- Share - copy and redistribute the material in any medium or format
- Adapt - remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms
_2020_-_7(2)_2024_-_Thumbnail.png)
