Modeling of Convolutional Neural Network Architecture for Recognizing The Pandava Mask
| Main Authors: | Sanjaya, Andi, Setyati, Endang, Budianto, Herman |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Dr. Soetomo
, 2020
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| Subjects: | |
| Online Access: |
https://ejournal.unitomo.ac.id/index.php/inform/article/view/2740 https://ejournal.unitomo.ac.id/index.php/inform/article/view/2740/pdf https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2740/572 https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2740/573 https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2740/574 https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2740/594 |
Daftar Isi:
- This research was conducted to observe the use of architectural model Convolutional Neural Networks (CNN) LeNEt, which was suitable to use for Pandava mask objects. The Data processing in the research was 200 data for each class or similar with 1000 trial data. Architectural model CNN LeNET used input layer 32x32, 64x64, 128x128, 224x224 and 256x256. The trial result with the input layer 32x32 succeeded, showing a faster time compared to the other layer. The result of accuracy value and validation was not under fitted or overfit. However, when the activation of the second dense process as changed from the relu to sigmoid, the result was better in sigmoid, in the tem of time, and the possibility of overfitting was less. The research result had a mean accuracy value of 0.96.
- Abstract - This research was conducted in an effort to observe looking for CNN model architecture that is suitable for use on the Pandava mask object. The data tested were as much as 200 class data. So there are 1000 trial data in this study. In experiments with LeNEt using the input layer 32x32, 64x64, 128x128, 224x224, 256x256 showed that the 32x32 input layer succeeded in showing a faster time than the other input layers, accuracy and validation accuracy are not underfit or overfit. However, when the second dense process is switched from relu to sigmoid, the result of sigmoid is better than relu in terms of time and the possibility of overfit is smaller than using relu.