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Skills Competency (UKK) is part of government intervention in ensuring the quality of education in the Secondary Vocational School aims to measure the achievement of a certain level of competency in students- appropriate competency skills. The C4.5 algorithm is the algorithm for classification learning machine learning and data mining that generate either a decision tree, each attribute selection is used in determining the best criteria for classifying the tuple. Feature selection is done to reduce irrelevant features and reduce the dimensions of the features in the data. The purpose of the research is to find out how competent the students- in their vocation as well as new strategies for educators in providing a more effective learning. Students- competency expertise data from department of computer engineering and networks in SMK TI Muhammadiyah Cikampek will be used. Overall the results of the validation of the model experiments that have the best influence is supplied with a set of test accuracy values of 96.875%, and 13 optimal attributes are selected. The utilization of the feature selection is to reduce the size of the input data that will be managed on processing and analysis. Therefore, in this study the C4.5 algorithm with feature selection can provide good and effective results.