Telur merupakan makanan yang memiliki gizi tinggi. Dijaman sekarang telah ada telur dengan omega-3 hasil rekayasa. Secara visual untuk membedakan telur ayam biasa dan telur ayam dengan omega-3 sangat sulit karena bentuk fisik dan warna telurnya terlihat sama. Bagian yang membedakan adalah kuning telur omega-3 agak kekuningan dan kuning telur biasa lebih kemerahan. Penelitian ini diciptakan sebuah sistem analis yang mampu mengenali telur berdasarkan tekstur dengan beberapa langkah dalam teknik pengolahan citra. Beberapa teknik pengolahan citra yang digunakan yaitu konversi citra RGB ke grayscale, perbaikan kualitas citra, menghilangkan noise dengan gaussian filter dan analisis citra menggunakan ekstraksi ciri statistik orde pertama dengan nilai parameter mean, standard deviasi. Berdasarkan pengujian diperoleh tingkat precision 87,93%, recall 96,22% dan accuracy 85% berdasarkan 140 data training dan 60 data uji.

In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.

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