Classification of batik in southern coast area of java using convolutional neural network method

Authors

  • Taufik Cahya Prayitna Universitas Ahmad Dahlan
  • Murinto Murinto Universitas Ahmad Dahlan

Abstract

Batik is a craft inherited from our ancestors from the archipelago which has a high aesthetic value. Batik has several kinds of motives. Perhaps only a few of the information related to batik can find out. Therefore, not everyone can know or recognize batik in the southern coastal areas of Java correctly. Convolutional Neural Network is a part of deep learning that can be used to recognize and detect objects in digital images. Convolutional Neural Network is a type of Artificial Neural Network that was created specifically so that it can work on data in the form of an array. Based on the results of the study, the results obtained were 100% accuracy for the training process and 99% for the testing process with 630 training data and 180 validation data. The accuracy results obtained by testing the model are 93,3 % with 90 test data. So it can be concluded that the CNN model that has been created can classify batik motifs well.

Downloads

Published

2021-07-13

Issue

Vol. 15 No. 3 (2021): September 2021

Section

Articles

License

Authors who publish with Jurnal Informatika (JIFO) 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 (CC BY-SA 4.0) 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.

 

 Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.