  In the field of object classification, identification based on object
variations is a challenge in itself. Variations include shape, size, color, and
texture, these can cause problems in recognizing and distinguishing objects
accurately. The purpose of this research is to develop a classification method
so that objects can be accurately identified. The proposed classification model
uses Voting and Combined Classifier, with Random Forest, K-NN, Decision Tree,
SVM, and Naive Bayes classification methods. The test results show that the
voting method and Combined Classifier obtain quite good results with each of
them, ensemble voting with an accuracy value of 92.4%, 78.6% precision, 95.2%
recall, and 86.1% F1-score. While the combined classifier with an accuracy
value of 99.3%, a precision of 97.6%, a recall of 100%, and a 98.8% F1-score.
Based on the test results, it can be concluded that the use of the Combined
Classifier and voting methods is proven to increase the accuracy value. The
contribution of this research increases the effectiveness of the Ensemble
Learning method, especially the voting ensemble method and the Combined
Classifier in increasing the accuracy of object classification in image
processing.
