Prazka Aldiyuda, . (2025) SISTEM KLASIFIKASI JENIS IKAN AIR LAUT MENGGUNAKAN ALGORITMA RESNET50 DAN EFFICIENTNET. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
This study aims to develop an image classification system for marine fish species using deep learning architectures based on transfer learning, namely ResNet50 and EfficientNet. The classification covers seven types of marine fish: Clownfish, Mackerel, Skipjack Tuna, Red Snapper, Grouper, Yellowtail Fusilier, and Barracuda. The ResNet50 model achieved an average recall of 0.98, precision of 0.97, and an f1-score of 0.97. In comparison, the EfficientNet model outperformed with scores: 0.99 for recall, precision, and f1-score. Evaluation results show that EfficientNet achieved 98,98% prediction accuracy, while ResNet50 achieved 96.94%. These findings indicate that EfficientNet provides superior performance in marine fish classification. This study contributes to the development of automated classification systems that can serve as accurate and efficient educational tools for identifying marine fish species.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511102] [Pembimbing 1: Supriyanto] [Pembimbing 2: Nurul Afifah Arifuddin] [Penguji 1: Indra Permana Solihin] [Penguji 2: Novi Trisman Hadi] |
| Uncontrolled Keywords: | image classification, marine fish, ResNet50, EfficientNet, deep learning, transfer learning |
| 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: | PRAZKA ALDIYUDA |
| Date Deposited: | 26 Aug 2025 10:08 |
| Last Modified: | 26 Aug 2025 10:08 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37483 |
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