The classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an
object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification
methods used, including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Decision Tree. To determine the
accuracy in detecting these objects, it is necessary to measure the accuracy of each used classification method. The object that became
simulation in this research was the object image of Guava and Pear fruit. Testing using confusion matrix. The results showed that the
Support Vector Machine (SVM) method was able to detect with an accuracy of 98.09%. Then the K-Nearest Neighbors (K-NN)
method with an accuracy of 98.06%, then the Decision Tree method with an accuracy of 97.57%. From the results of the accuracy test,
it can be concluded that basically these three classification methods have high accuracy with a difference of 0.49% and the overall
average accuracy of the classification of the three methods is 97.89%.