Detecting Chili Ripeness Using YOLOv11 - Pratama, Andika Ramadian; Fajriani, Alfiah; Rifai, Sitti Najmia (Sistemasi: Jurnal Sistem Informasi, 2026)
This study aims to develop a deep learning-based chili ripeness detection system using the YOLOv11 model. Chili ripeness is classified into three categories: unripe, semi-ripe, and ripe. The dataset consists of 150 original images, which were expanded to 300 images to increase data variation. Model training was conducted using the Roboflow platform, while accuracy testing was performed in Google Colab through an image upload-based processing method. The experimental results show that the model achieved an accuracy of 93.94%, with a precision of 94.21%, recall of 93.94%, and an F1-score of 93.94% on the test dataset. This system is expected to support the automation of chili sorting based on ripeness levels.