Muhammad Fadillah, . (2024) KLASIFIKASI CITRA ABJAD BAHASA ISYARAT INDONESIA MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
This research proposes the use of a convolutional neural network (CNN) model to classify images of the Indonesian sign language alphabet. The dataset consists of 26 classes, each representing a letter from the Indonesian sign language alphabet. The model is trained on a dataset containing 218 images and evaluated on a test dataset of 63 images. The results show that the model achieves an accuracy of 95.24% on the test set. While the model demonstrates high accuracy for most classes, it encounters difficulties in recognizing the ‘O’, ‘Q’, and ‘V’ classes, resulting in a low recall and f1-score for these specific classes. For future work, further evaluation of the model is recommended to address challenges in improving performance, particularly for these classes that hard to recognize.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1810511064] [Pembimbing: Indra Permana Solihin] [Penguji 1: Iin Ernawati] [Penguji 2: Muhammad Panji Muslim] |
| Uncontrolled Keywords: | Indonesian Sign Language, Image Classification, CNN, Alphabet Recognition |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | MUHAMMAD FADILLAH |
| Date Deposited: | 05 Sep 2024 03:54 |
| Last Modified: | 05 Sep 2024 03:54 |
| URI: | http://repository.upnvj.ac.id/id/eprint/31666 |
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