Implementasi Yolov11 pada Sistem Deteksi Cacat Biji Kopi Pasca-Roasting

Abstract

Coffee bean quality after roasting is one of the important factors in determining the quality of coffee products. The process of identifying and sorting defective coffee beans is generally still performed manually, which may lead to inconsistencies and observation errors. This study aims to implement the You Only Look Once version 11 (YOLOv11) method based on Convolutional Neural Network (CNN) in an automatic post-roasting coffee bean defect detection and classification system. The research stages include image data collection, preprocessing, object labeling, dataset splitting into training, validation, and testing data, as well as model training using several hyperparameter configurations. Testing was conducted using variations in data splitting, optimizer, learning rate, and confidence threshold to obtain optimal model performance. The dataset used consisted of 694 images, including 290 primary images and 404 augmented images. The results showed that the 90:5:5 dataset splitting scenario achieved the highest mAP@50 value of 0.948. However, the 70:20:10 configuration was selected as a more stable and representative configuration because it provided a better balance between training, validation, and testing data. The use of the SGD optimizer with a learning rate of 0.01 and a confidence threshold of 0.5 produced more stable detection performance. The resulting model achieved a Mean Average Precision (mAP@50) value of 0.946 and was able to automatically detect and classify coffee bean defects effectively under various testing conditions.