Klasifikasi Bobot Telur Ayam Ras menggunakan Visi Komputer dan Segmentasi Citra - Supiyandi Supiyandi; Warda Hamidah; Nazwa Alya Faradita; Arizka Anggraini; Adisty Maysandra (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2025)
This study aims to classify chicken eggs based on their physical size using the concept of computer vision and image segmentation techniques. Compared to the standard methods that have been used so far, this alternative technology is expected to help standardize measurements, cost efficiency, and work effectiveness. In this study, the classification of chicken eggs was carried out using image segmentation and regression analysis. Thus, it is expected that the classification of chicken eggs will have increasingly accurate values. After the image is taken using a webcam, the image segmentation process is used to divide the image into homogeneous areas based on the RGB (true color) color intensity similarity standard. Regression analysis is used to study and measure the relationship between the number of pixels and the weight of the object. The number of pixels indicating the area of ​​the object is the result of image segmentation, which will be entered into the regression equation to calculate the weight (grams). The results showed that the color characteristics of chicken eggs have a normalization of R at least 0.41 and a normalization of G at least 0.3. In addition, the classification test has an accuracy of 100% (36/36) and a weight estimation accuracy of 42 percent (15/36).