Sensorless Synchronous Motors Classification Using Random Forest and Linear Support Vector Classifier
Fahri Ari Rahman, R R Julviar, Intan Nurma Yulita · IOP Conference Series Earth and Environmental Science · 2019
Automation system become increasingly complex and it needed to be monitored actively. Due to wide variance tasks to be done not suprising that the complexity problem occurs. The failure of the system can impact to economic loss for a company. For this purpose, this paper compare the methods to classify sensorless synchronous motors task to investigate the accuracy and precision of random forest classification and linear Support Vector Classifier (SVC). With cross-validation, the result show that the average accuracy is 99.816% with random forest and 66.04% with linear SVC. As the conclution, random forest classification is more precise than SCV method in this field.