The classifier model for prediction quail gender after birth based on external factors of quail egg

Abstract

This paper proposes the classification model to identify the quail gender which considered from only the external factors of quail egg. The six classifier models were studied including decision tree-J48, ADTree, Support Vector Machine-LibSVM, SMO, Multilayer Perceptron, and NaïveBayes. We evaluated each classifier model using 10-fold cross validation with 120 subsets obtained from 661 quail eggs. A number of external factors acquired from quail eggs in each subset were difference that was the combination among seven external factors. The result showed that the J48 decision tree achieved the highest accuracy up to 80% with only using 5 external factors. Therefore, this research can be beneficial to quail farmers in reducing costs to feed male quail and can be value added to quail eggs, as well.