Klasifikasi Pemilihan Sopir Pengangkut Ruminansia Besar (Sapi) dengan Algoritma Support Vector Machine (SVM)
Abstract
The condition of livestock is affected by the transportation process both before transportation, during transportation and at the end of transportation. The three main things that are affected by transportation are thermal pressure, physiological pressure and physical condition pressure. Bad factors due to logistics usually result in the feeling of discomfort felt by animals during the trip. Cattle logistics can cause stress to cows. This is due to rough handling during transport, poor shipping lane conditions, overcrowding, inadequate air inlets, extremes of temperature and humidity, and wind speeds. Cows that suffer from stress have an impact on reducing the weight of the cow. Classifying drivers for transporting large ruminants (cows) is important in order to be able to select good drivers so that animal welfare during the trip can be considered. The research methodology that will be applied in this study is by using the Knowledge Discovery In Databases (KDD) methodology. Finding patterns from large and complex data, obtaining correct, new, and useful information is the result of the Knowledge Discovery in Databases (KDD) analysis process with the stages of data selection, preprocessing, transformation, modeling, and evaluation. The algorithm used is the Support Vector Machine (SVM) using four kernels, namely the linear kernel, the polynomial kernel, the sigmoid kernel, and the RBF kernel. Data division using traintestsplit, divided into two scenarios, namely 70:30 and 80:20. Classification is divided into 8 classes. The results of the research after testing the model by calculating Accuracy, Precision, Recall, and F1-Score. The best result is a linear kernel with an accuracy of 100%, a precision of 100%, a recall of 100%, and an f1-score of 100% in the 80:20 division scenario.
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