Zakiyya Halimatus Sa'diyah, Zakiyya (2025) IMPLEMENTASI YOLOV8 UNTUK MENENTUKAN TINGKATAN KATEGORI LUKA BAKAR. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
This study aims to develop and implement a burn detection and classification system using the YOLOv8 model on medical images. The YOLOv8 model was chosen for its ability to perform fast and accurate object detection. The dataset used in this study consists of burn images obtained from direct observations and data scraping. After preprocessing, including resizing and image augmentation, the YOLOv8 model was trained and tested to detect three levels of burn severity: first-degree, second-degree, and third-degree burns. The results show that the YOLOv8 model is able to provide a satisfactory detection accuracy with Accuracy, Precision, Recall and mAP values of 70%, 66.8%, 60.8% dan 62.8%., respectively. The detection results under certain conditions can still be improved, particularly in terms of accuracy on images with low lighting or varying orientations. This study is expected to contribute to the development of a practical and accurate AI-based burn diagnosis system, supporting faster and more effective medical treatment. Keywords: Burn Detection, YOLOv8, Artificial Intelligence, Burn Classification, Detection Accuracy.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511156] [Pembimbing 1: Neny Rosmawarni] [Pembimbing 2: Muhammad Panji Muslim] [Penguji 1: Widya Cholil] [Penguji 2: Kharisma Wiati Gusti] |
| Uncontrolled Keywords: | Burn Detection, YOLOv8, Artificial Intelligence, Burn Classification, Detection Accuracy |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QM Human anatomy R Medicine > RD Surgery R Medicine > RL Dermatology T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | ZAKIYYA HALIMATUS SA'DIYAH |
| Date Deposited: | 13 Jul 2025 21:09 |
| Last Modified: | 13 Jul 2025 21:10 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37385 |
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