Identifikasi Kondisi Kesehatan Ayam Petelur Berdasarkan Ciri Warna HSV Dan Gray Level Cooccurence Matrix (GLCM) Pada Citra Jengger Dengan Klasifikasi K-Nearest Neighbour - Maharani Tri Hastuti; Agus Wahyu Widodo; Candra Dewi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
One way of the good maintenance to laying chickens is separate the healthy and unhealthy chicken in defference cage quickly and correctly. But, in reality there's so many stock farmer who don't have fast response about the issue. Other than that, in the rural area we couldn't find any veterinarian or farm expert easily. So, we need a system to identify the condition of chicken health automatically. In this case, clinical symptoms in sick laying chickens can be observed through changes in color brightnes and texture in the wattle. Healthy laying chicken has bright red wattle and it tends to feel rough. The solution that can be applied to this problem is image processing with HSV color and graylevel coocurence matrix (GLCM) feature extraction. In this study GLCM method oriented by 4 angles that are 00, 450,900 and 1350 with d=1. From the extraction results we will get the values of HSV and statistic feature of GLCM such as entropy, energy, homogeneity, contrast and correlation for K-NN classification's input. A total of 26 testing data features will be calculated its euclidean distance with training data to search classes from input data. Based on the result of this study, the best accuracy obtained when classification with (GLCM 4 directions or 00) + all component of HSV and the number K = 3, K = 11 or K = 15 that is 100% correctness.