Analisis Kinerja Algoritma Kirsch dan Robinson dalam Deteksi Tepi Citra - Diana; Septa Riansyah (PPPM Institut Teknologi Pagar Alam, 2026)
One of the important stages in digital image processing is edge detection, which serves to determine the boundaries of objects in an image. This process is very important for various applications, such as image segmentation, pattern recognition, and object analysis. Two very popular algorithms in gradient operator-based edge detection are Kirsch and Robinson. The Kirsch algorithm uses eight kernels with compass directions to detect pixel intensity changes maximally, while the Robinson algorithm uses eight simpler kernels with a lighter computational approach. The aim of this research is to examine how both algorithms detect image edges based on the sharpness of the results, sensitivity to noise, and computational efficiency. The study was conducted by testing both algorithms on several grayscale test images and comparing the visual and quantitative edge detection results using the parameters of accuracy, precision, recall, and F-Measure. The analysis results show that the Robinson algorithm is more efficient in the computation process but produces smoother edges. The Kirsch algorithm, on the other hand, produces sharper and more detailed edge detection but requires longer computation time. Therefore, which algorithm is most suitable for the application depends on whether the priority is on process efficiency or detection quality.