Kamila Dinia Kartono, . (2025) IMPLEMENTASI MODEL PRETRAINED CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK KLASIFIKASI VARIETAS SAWI BERBASIS GUI. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
The visual similarity among mustard green varieties often makes it difficult for people, especially the younger generation, to distinguish between them based on physical characteristics. Therefore, this study was conducted with the aim of identifying five mustard green varieties: sawi caisim, kailan, pakcoy, putih, and pahit. This research utilizes the Convolutional Neural Network (CNN) algorithm based on transfer learning with the VGG16 architecture to build a classification model. To enhance the model’s ability to recognize images that do not belong to the five main classes, a second model was developed by adding an unknown class. Training results showed that the best model for classifying the five mustard green varieties achieved an accuracy of 98,82%. Meanwhile, the model with the added unknown class achieved an accuracy of 99.02%, indicating that the addition of this class did not reduce the classification performance for the five main varieties. The developed model was integrated into a Graphical User Interface (GUI) that displays classification results along with the identified mustard green variety’s characteristics.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511100] [Pembimbing 1: Supriyanto] [Pembimbing 2: Nurhafifah Matondang] [Penguji 1: Indra Permana Solihin] [Penguji 2: Nurul Afifah Arifuddin] |
| Uncontrolled Keywords: | CNN, Graphic User Interface, Image Classification, Mustard Green, VGG16. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | KAMILA DINIA KARTONO |
| Date Deposited: | 06 Aug 2025 07:36 |
| Last Modified: | 06 Aug 2025 07:36 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37474 |
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