Identification of Incung Characters (Kerinci) to Latin Characters Using Convolutional Neural Network
Incung script is a legacy of the Kerinci tribe located in Kerinci Regency, Jambi Province. On October 17, 2014, the Incung script was designated by the Ministry of Education and Culture as an intangible heritage property owned by Jambi Province. But in reality, the Incung script is almost extinct in...
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| Format: | EJournal Article |
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IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2022-04-30.
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| LEADER | 02690 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_70939 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Putri, Tesa Ananda |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Suratno, Tri |e author |
| 700 | 1 | 0 | |a Khaira, Ulfa |e author |
| 245 | 0 | 0 | |a Identification of Incung Characters (Kerinci) to Latin Characters Using Convolutional Neural Network |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2022-04-30. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/70939 | ||
| 520 | |a Incung script is a legacy of the Kerinci tribe located in Kerinci Regency, Jambi Province. On October 17, 2014, the Incung script was designated by the Ministry of Education and Culture as an intangible heritage property owned by Jambi Province. But in reality, the Incung script is almost extinct in society. This study aims to identify the characters of the Incung (Kerinci) script with the output in the form of Latin characters from the Incung script. The classification method used is the Convolutional Neural Network (CNN) method. The dataset used as many as 1400 incung character images divided into 28 classes. In this study, an experiment was conducted to obtain the most optimal model. Showing the results using the CNN method during the training process that the accuracy of the training data reaches 99% and the accuracy of the testing data reaches 91% by using the optimal hyperparameters from the tests that have been done, namely batch size 32, epoch 100, and Adam's optimizer. It evaluates the CNN model using 80 images in words (a combination of several characters) with 4 test scenarios. It shows that the model can recognize image data from scanning printed books, digital writing test data, test data with images containing more than two characters, and check images with different font sizes | ||
| 540 | |a Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Information System | ||
| 690 | |a Incung Script; Convolutional Neural Network; Deep Learning; Image; Pattern Recognition | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 2 (2022): April; 205-216 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/70939/33701 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/70939 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/70939/33701 |z Get Fulltext |