ALGORITHM COMPARISON AND FEATURE SELECTION FOR CLASSIFICATION OF BROILER CHICKEN HARVEST
Abstract
Broiler chickens are the result of superior breeds that produce a lot of meat. In practice, however, many breeders experience crop failure, which has a serious impact on the economy and can also affect farmer quality, resulting in sanctions. The value of the performance index produced at harvest indicates the success rate of harvesting broiler chickens. Broiler crop yield data can be used to help classify broiler crop yield data using an approach method. The CRISP-DM (Cross Industry Standard Process for Data Mining) method was used in this study's data mining technique. This study compares 3 classification algorithms to determine the best algorithm and 3 feature selection methods to determine the best method for improving algorithm performance. According to the findings of this study, the Random Forest algorithm is the best algorithm for classifying harvest data, with an accuracy rate of 89.14 percent. The best way to improve the algorithm's performance is to use the Backward Elimination method, which can increase the accuracy by 7.53 percent. As a result, the Random Forest + Backward Elimination algorithm yields an accuracy value of 96.67 percent. According to this study, the factors that influence crop yield increase are FCR, number of harvests, and body weight.
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