<![CDATA[The high consumption of native chickens by people in Indonesia indirectly demands the field of native chicken farming so that the need for the amount of meat can be met. In the initial stage, namely breeding, breeders need to produce chicken eggs to be used as seeds, where the success of hatching eggs can be identified by monitoring at the age of 8 days of the incubation period. These eggs can be identified earlier at the age of 5 days of incubation but require good eye ability because the chicken embryos that are formed are still small. A fertility classification system is needed so that eggs can be identified whether they are capable of hatching or not. The system in this study uses the Gray Level Co-occurrence Matrix (GLCM) method for the feature extraction process from an egg image. The extraction results are reprocessed using the Learning Vector Quantization (LVQ) method so that the results of the classification of the egg image classification are fertile or infertile. The data used are images of eggs that have gone through an incubation period of 5 days and are photographed with the aid of a flashlight. The results of this study, the GLCM method was able to extract features from the image of native chicken eggs, either fertile or infertile, with a significant value. Whereas for the LVQ method, the best test results are using learning rate of 0.05 and for GLCM using 5 features with 0 degree for angle orientation, with an accuracy value of 88.13%.]]>
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