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.

基于物体分类的识别方法研究中，对象的变化是一个挑战。本研究提出了一种分类方法，使用投票和组合分类器，结合随机森林、K-NN、决策树、SVM 和朴素贝叶斯分类方法。测试结果表明，投票方法和组合分类器的准确率分别为 92.4% 和 99.3%。研究结果显示，使用组合分类器和投票方法可以提高对象分类准确性，增加了集成学习方法的有效性。