Tinjauan Penerapan CNN dan YOLO pada Pengolahan Citra Agraria Medis Industri Cerdas - Robby Saputra; Rianto, Yan; Kusuma, Muhammad Romadhona (Universitas Muhammadiyah Riau, 2026)
The Rapid growth of Artificial Intelligence (AI), particularly Deep Learning, is driving significant transformations in digital image processing in the argricultural, medical, and smart industrial sectors. Two approaches are most dominant in this research Convolutional Neural Network (CNN) for image classification and You Only Look Once (YOLO) for real-time object detection. The purpose of this reasearch is to systematically review the application, performance, and defense of CNN and YOLO in various domains with different data characteristics. The method used is a Systematic Literatur Review (SLR) of the latest relevant scientific publications, focusing on evaluation matrics such as accuracy, pression, recall, F1-score, and Mean Average Precision (mAP). The review results show that CNN excels in image classification tasks with a high level of accuracy, especially on data with relatively stabel visual patterns, while YOLO is more effective in applications that demand inference speed and direct object detection. However, several major limitations were found, including decreased performance in extreme lighting conditions, complex backgrounds, small objects, and visual similarity between classes. It is concluded that the choice of architecture must be adjusted to the characteristics of the data and application needs,