Analisis Validasi dan Evaluasi Model Deteksi Objek Varian Jahe Menggunakan Algoritma Yolov5

Abstract

Object detection is one of the important techniques in the field of computer vision and image processing. In this study, a validation and evaluation analysis of the object detection model of ginger variants using the YOLOv5 algorithm with a Convolutional Neural Network (CNN) approach was carried out. The dataset used consists of various ginger variants taken from several sources. The dataset is divided into two parts, namely the training data and the testing data. Model training is carried out on the training data using the YOLOv5 algorithm with a CNN approach. Testing is carried out on the testing data to measure the model's performance in detecting ginger variants. The analysis results showed that the object detection model of ginger variants using the YOLOv5 algorithm with a CNN approach can provide quite accurate results with a detection accuracy rate of 93,9%, So, the detection of ginger variants can be a useful recommendation as a means of varieties authenticity verification utilizing diverse ginger variants. However, there were several challenges faced in processing the dataset, such as variations in lighting and different angles of image capture. Therefore, this study provides recommendations for improving the dataset and optimizing parameter settings to improve the performance of the object detection model of ginger variants using the YOLOv5 algorithm with a CNN approach.