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In the field of object classification, identification based on objectvariations is a challenge in itself. Variations include shape, size, color, andtexture, these can cause problems in recognizing and distinguishing objectsaccurately. The purpose of this research is to develop a classification methodso that objects can be accurately identified. The proposed classification modeluses Voting and Combined Classifier, with Random Forest, K-NN, Decision Tree,SVM, and Naive Bayes classification methods. The test results show that thevoting method and Combined Classifier obtain quite good results with each ofthem, 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 accuracyvalue 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 CombinedClassifier and voting methods is proven to increase the accuracy value. Thecontribution of this research increases the effectiveness of the EnsembleLearning method, especially the voting ensemble method and the CombinedClassifier in increasing the accuracy of object classification in imageprocessing.

Object Classification Model Using Ensemble Learning with Gray-Level Co-Occurrence Matrix and Histogram Extraction