Modifikasi Arsitektur dalam Convolutional Neural Network untuk Klasifikasi Batik Lampung dan Batik Yogyakarta

Yonathan Octavianus; Donny Avianto

doi:10.35870/jimik.v6i1.1223

Vol. 6 No. 1 (2025) • Articles

Open Access

Modifikasi Arsitektur dalam Convolutional Neural Network untuk Klasifikasi Batik Lampung dan Batik Yogyakarta

Yonathan Octavianus

Universitas Teknologi Yogyakarta

Donny Avianto

Universitas Teknologi Yogyakarta

DOI: https://doi.org/10.35870/jimik.v6i1.1223

Published: January 10, 2025 Pages: 522-535

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Abstract

Batik is one of the unique forms of Indonesian culture. On October 2, 2009 UNESCO (United Nations Educational, Scientific, and Cultural Organization) designated batik as a Masterpiece of the Oral and Intangible Heritage of Humanity. One of the batik heritages of our ancestors is Lampung Batik and Yogyakarta Batik, where both batik have their own differences and uniqueness, so that we as Indonesian people must maintain and preserve the cultural heritage of our ancestors by creating a system that can determine both batik without using instinct or based on recommendations from others which can still cause errors. In previous studies, classification using Multikernel SVM managed to achieve an accuracy of 100%. There are also those who use CNN(Convolutional Neural Network)-Sobel with an accuracy of 91,2% in the training process and 91,8% in the validation process. The problems experienced in previous studies were the limitations of the dataset and the model testing process which was still not optimal so that it did not get satisfactory results so that in this study the Convolutional Neural Network method will be used with 6 architectures, 3 of which are unModified architectures, namely MobileNetV2, DenseNet121, and Xception. And 3 Modified architectures, namely MobileNetV2 (Modified), DenseNet121 (Modified), and Xceptipn (Modified). The selection of the three architectures is because it has a very large number of layers so that it can calculate a very large amount of data and produce the appropriate output. The best results obtained in this study were the Modified architecture, namely Xception (Modified) with an accuracy of 100%, Precision 97%, Recall 94%. F1 Score 92%, and Loss 0,0066 in the 30th epoch experiment and learning rate 0,0001 so that Xception became the best model in the Modified architecture (Modified). This research is expected to be able to provide a renewable technology system to ordinary people who do not know Lampung Batik and Yogyakarta Batik to be able to distinguish between the two specifically so as to minimize errors in analyzing or when buying the desired Batik

Keywords:

Convolutional Neural Network Modification Lampung Batik Yogyakarta Batik Architecture

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Author Biographies

Yonathan Octavianus Universitas Teknologi Yogyakarta

Program Studi Informatika, Fakultas Sains & Teknologi, Universitas Teknologi Yogyakarta, Kabupaten Sleman, Provinsi Daerah Istimewa Yogyakarta, Indonesia.

Donny Avianto Universitas Teknologi Yogyakarta

Program Studi Informatika, Fakultas Sains & Teknologi, Universitas Teknologi Yogyakarta, Kabupaten Sleman, Provinsi Daerah Istimewa Yogyakarta, Indonesia.

How to Cite

Octavianus, Y., & Avianto, D. (2025). Modifikasi Arsitektur dalam Convolutional Neural Network untuk Klasifikasi Batik Lampung dan Batik Yogyakarta. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(1), 522-535. https://doi.org/10.35870/jimik.v6i1.1223

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References

Total: 17 References

Agustiani, S., Arifin, Y. T., Junaidi, A., Wildah, S. K., & Mustopa, A. (2022). Klasifikasi Penyakit Daun padi Menggunakan random forest dan color histogram. Jurnal Komputasi, 10(1), 65-74. https://doi.org/10.23960/komputasi.v10i1.2961.
Andono, P. N., & Rachmawanto, E. H. (2021). Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik. Jurnal Resti (Rekayasa Sistem dan Teknologi Informasi), 5(1), 1-9. https://doi.org/10.29207/resti.v5i1.2615.
ATMAJA, W. P., & LUSIANA, V. (2023). Klasifikasi Jenis Batik Pekalongan Menggunakan Citra Hsi Dengan Metode K-Nearest Neighbor. Jurnal INSTEK (Informatika Sains dan Teknologi), 8(1), 1-7. https://doi.org/10.24252/instek.v8i1.35967.
Fahcruroji, A. R., Wijaya, M. Y., & Fauziah, I. (2024). IMPLEMENTASI ALGORITMA CNN MOBILENET UNTUK KLASIFIKASI GAMBAR SAMPAH DI BANK SAMPAH. PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer, 11(1), 45-51. https://doi.org/10.30656/prosisko.v11i1.8101.
Harahap, R. I. J., & Khairani, S. (2024). Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun. Jurnal Ilmu Komputer dan Sistem Informasi, 3(2), 135-146. https://doi.org/10.70340/jirsi.v3i2.123.
Hawibowo, M. S., & Muhimmmah, I. (2024). Aplikasi Pendeteksi Tingkat Kematangan Pepaya menggunakan Metode Convolutional Neural Network (CNN) Berbasis Android. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 10(1), 162-170. https://doi.org/10.26418/jp.v10i1.77819.
Juwita, A. R., Al Mudzakir, T., Pratama, A. R., Husodo, P., & Sulaiman, R. (2021). IDENTIFIKASI CITRA BATIK DENGAN METODE CONVOLUTIONAL NEURAL NETWORK. BUANA ILMU, 6(1), 192-208.
Khoiruddin, M., Junaidi, A., & Saputra, W. A. (2022). Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network. Journal of Dinda: Data Science, Information Technology, and Data Analytics, 2(1), 37-45. https://doi.org/10.20895/dinda.v2i1.341.
Kusuma, J., Rosnelly, R., & Hayadi, B. H. (2023). Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron. Journal of Applied Computer Science and Technology, 4(1), 1-6. https://doi.org/10.52158/jacost.v4i1.484.
Masa, A. P. A., & Hamdani, H. (2021). Klasifikasi Motif Citra Batik Menggunakan Convolutional Neural Network Berdasarkan K-means Clustering. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(4), 1292-1301.
Maulana, I., Sastypratiwi, H., Muhardi, H., Safriadi, N., & Sujaini, H. Implementasi Convolutional Neural Network (CNN) untuk Klasifikasi Motif Batik pada Aplikasi Computer Vision Berbasis Android. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 9(3), 384-393. https://doi.org/10.26418/jp.v9i3.69496.
Nisa, C., & Candra, F. (2024). Klasifikasi Jenis Rempah-Rempah Menggunakan Algoritma Convolutional Neural Network: Classification of Spice Types Using the Convolutional Neural Network Algorithm. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(1), 78-84. https://doi.org/10.57152/malcom.v4i1.1018.
Pradana, D., Alghifari, M. L., Juna, M. F., & Palaguna, D. (2022). Klasifikasi Penyakit Jantung Menggunakan Metode Artificial Neural Network. Indonesian Journal of Data and Science, 3(2), 55-60. https://doi.org/10.56705/ijodas.v3i2.35.
Putra, A. E., Naufal, M. F., & Prasetyo, V. R. (2023). Klasifikasi Jenis Rempah Menggunakan Convolutional Neural Network dan Transfer Learning. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 9(1), 12-18. https://doi.org/10.26418/jp.v9i1.58186.
Raharja, A. D. R. J., & Widyastuti, W. (2022, July). Pengenalan Motif Batik Jogja dengan Jaringan Syaraf Tiruan Perambatan Balik. In Seminar Nasional Riset & Inovasi Teknologi (Vol. 1, No. 1, pp. 214-222).
Utari, L., & Zulfikar, A. (2023). Penerapan Convolutional Neural Networks Menggunakan Edge Detection Untuk Identifikasi Motif Jenis Batik. TeknoIS: Jurnal Ilmiah Teknologi Informasi dan Sains, 13(1), 110-123. https://doi.org/10.36350/jbs.v13i1.184.
Yanuardi, M. I., Aminudin, A., & Faiqurahman, M. Analisis Perbandingan Efektivitas Arsitektur Restful dan Arsitektur Grpc pada Implementasi Web Service. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 10(2), 333-341. https://doi.org/10.26418/jp.v10i2.80845.