<![CDATA[Decision tree is one of the popular methods in data analysis and machine learning. The C4.5 algorithm is one of the most widely used decision tree algorithms because of its ability to produce decision rules that can be understood easily. However, various variations and developments of other decision tree algorithms have emerged, offering improved performance and new features. This study aims to carry out a comparative analysis between the C4.5 decision tree algorithm and several other decision tree algorithms that have been developed. The method used in this research is a systematic literature review, in which the researcher conducts a structured search and evaluation of relevant scientific articles. Researchers will compare the performance of the C4.5 algorithm with other algorithms based on several criteria, including predictive accuracy, computational complexity, interpretability of decision rules, and ability to handle unbalanced data. The results of the analysis show that the selection of a decision tree algorithm must be based on the specific purpose of the analysis and the characteristics of the data used. If the interpretability of decision rules is a major factor, the C4.5 algorithm remains a good choice. However, if predictive accuracy and handling of unbalanced data is a priority, algorithms such as Random Forest, Naive Bayes, or KNN may be a better choice.]]>
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