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All Journal Repeater: Publikasi Teknik Informatika dan Jaringan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Egga Naufal Daffa Tanadi
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Computer Science & IT

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Sistem Pendeteksi Penyakit Kanker Kulit Menggunakan Convolutional Neural Network Arsitektur YOLOv8 Berbasis Website Egga Naufal Daffa Tanadi; Dhian Satria Yudha Kartika; Abdul Rezha Efrat Najaf
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 3 (2024): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v2i3.124

Abstract

Skin cancer has high incidence and fatality rates, making accurate and rapid detection crucial. This study developed a web-based skin cancer detection system using YOLOv8. The model detects seven types of skin cancer using a dataset of 17.366 annotated images. Methods included data collection, pre-processing, augmentation, model training, and performance evaluation using precision, recall, and mean Average Precision (mAP). Results show that the YOLOv8 model achieved a precision of 0.975 and a recall of 0.969. Evaluation with a confusion matrix demonstrated strong detection capabilities. A web interface was developed to allow users to upload images and view detection results in real-time. The YOLOv8-based skin cancer detection system provides accurate results and can be used as a tool for early diagnosis.
Sistem Pendeteksi Penyakit Kanker Kulit Menggunakan Convolutional Neural Network Arsitektur YOLOv8 Berbasis Website Egga Naufal Daffa Tanadi; Dhian Satria Yudha Kartika; Abdul Rezha Efrat Najaf
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 2 No. 3 (2024): Agustus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v2i3.224

Abstract

Skin cancer has high incidence and fatality rates, making accurate and rapid detection crucial. This study developed a web-based skin cancer detection system using YOLOv8. The model detects seven types of skin cancer using a dataset of 3500 annotated images. Methods included data collection, pre-processing, augmentation, model training, and performance evaluation using precision, recall, and mean Average Precision (mAP). Results show that the YOLOv8 model achieved a precision of 0.975 and a recall of 0.969. Evaluation with a confusion matrix demonstrated strong detection capabilities. A web interface was developed to allow users to upload images and view detection results in real-time. The YOLOv8-based skin cancer detection system provides accurate results and can be used as a tool for early diagnosis.
Co-Authors Abdul Rezha Efrat Najaf Dhian Satria Yudha Kartika