<![CDATA[A decision tree algorithm or commonly called a decision tree is a classification method of data mining. The decision tree has one type of algorithm model, namely the C4.5 algorithm. The C4.5 decision tree algorithm is easy to understand because it has a tree-like structure in general. The C4.5 algorithm in handling quantitative data is often less efficient and effective. So to minimize information loss and time complexity, we can improvise the dataset on the numeric attributes when Preprocessing the data. Improvisation is done by using the mean and median on the numerical attributes to get a threshold value for implementing the C4.5 algorithm from the training data. Evaluation of the system used in this study uses a confusion matrix. Confusion matrix as a benchmark for testing the classification method using data testing. In this study, the dataset is partitioned into three scenarios. In scenario 1 with 70% training data and 20% testing data, the highest accuracy is 75%. The improvisation of the mean and median on the numerical attributes in the C4.5 algorithm can use in this scenario.]]>
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