Fadlil, Abdul and Umar, Rusydi and Gustina, Sapriani Similarity-AF-Mushroom Images Identification Using Orde 1 Statistics Feature Extraction.
![[thumbnail of Similarity-AF-Mushroom Images Identification Using Orde 1 Statistics Feature Extraction.pdf]](http://eprints.uad.ac.id/style/images/fileicons/text.png) |
Text
Similarity-AF-Mushroom Images Identification Using Orde 1 Statistics Feature Extraction.pdf Download (1MB) |
Abstract
There are many kinds of mushrooms difficult to identified manually. Because of that, a certain system that can be used to identify mushrooms is needed. One feature that artificial intelligence has is image identification. One image that can be identified mushroomimage. Mushroom image identification can contribute to artificial intelligence technology development. Computer-based mushroom image identification can be done by conducting a segmentation process that converts the original image to a grayscale image. The mushroom image pattern characteristics are selected and separated using a feature extraction process.
Mushrooms feature extraction conducted by using orde 1 statistics. Feature extraction results are classified using the Artificial Neural Network method with the Backpropagation
Algorithm. Classification process carried out by training and testing with neurons variations 5, 10, 15 and 20, while hidden layers are 0.1, 0.3, 0.5, 0.7, and 0.9 with 10,000 times iteration. 30 images that are consist of 15 images for training data and 15 images for test data. From research can be seen that mushroom image identification using orde 1 statistics features extraction with artificial neuron network has the best result with 93% accuracy on neuron 20. Mushroom’s image identification system that is developed can be implemented in other applications.
| Item Type: | Artikel Umum |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisi / Prodi: | Master (Magister) > Master of Technology Informatica (Magister Teknologi Informatika) |
| Depositing User: | Drs. Abdul Fadlil, M.T., Ph.D. |
| Date Deposited: | 22 Aug 2022 01:21 |
| Last Modified: | 22 Aug 2022 01:28 |
| URI: | http://eprints.uad.ac.id/id/eprint/36377 |
Actions (login required)
 |
View Item |