Implementasi Algoritma Yolo untuk Deteksi Kebusukan pada Sayur Kembang Kol

  • Alexander Ibrahim Universitas Udayana
  • I Wayan Supriana Universitas Udayana
##plugins.pubIds.doi.readerDisplayName## https://doi.org/10.24843/JNATIA.2024.v03.i01.p19

Abstrak

This research utilizes the YOLOv8 algorithm to detect spoilage in cauliflower vegetables. Image data was collected from Google, processed using Roboflow, and tested using Google Colab. The study results indicate an accuracy of 59%, recall of 58%, and MAP of 60%. The YOLOv8 algorithm significantly contributes to image recognition and visual data processing. Additionally, the article discusses the application of the YOLOv8 algorithm for object detection in 360-degree panoramic images. The training process was conducted to recognize objects in the images, and evaluation was performed using a confusion matrix and mAP50. The evaluation results demonstrate the model's good performance in object recognition. Several references cited in the article are also included.

Diterbitkan
2024-11-01
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IBRAHIM, Alexander; SUPRIANA, I Wayan. Implementasi Algoritma Yolo untuk Deteksi Kebusukan pada Sayur Kembang Kol. Jurnal Nasional Teknologi Informasi dan Aplikasnya, [S.l.], v. 3, n. 1, p. 161-168, nov. 2024. ISSN 3032-1948. Tersedia pada: <http://103.29.196.112/index.php/jnatia/article/view/116022>. Tanggal Akses: 04 mar. 2026 doi: https://doi.org/10.24843/JNATIA.2024.v03.i01.p19.

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