<![CDATA[Lecturer performance is an important factor in improving the quality of higher education, because lecturers not only act as educators, but also as researchers and community service. However, lecturer performance assessment often faces obstacles, such as the lack of uniform evaluation standards, a tendency for subjectivity in assessments, and limited evaluation instruments capable of assessing performance comprehensively. To overcome these problems, a data-driven approach is needed that can provide objective and measurable analysis results. One method that can be used is data mining with the C4.5 algorithm, which is a decision tree-based classification algorithm. This study aims to apply the C4.5 algorithm to measure lecturer performance achievements based on historical data that includes various indicators of the tridharma of higher education. The research stages include problem identification, literature review, data collection, selection of analysis techniques, implementation of the C4.5 algorithm with the help of RapidMiner software, and analysis of test results. The resulting classification model is visualized in the form of a decision tree so that it is easy to understand and can be used as a basis for evaluation. The test results show that the C4.5 algorithm is able to produce a classification model with an accuracy level of 86.67%. This demonstrates that C4.5 is effective in processing lecturer performance data and producing more objective and transparent evaluations, while also reducing the potential for subjectivity in assessments. This research provides a strategic contribution to supporting managerial decision-making in higher education, particularly in formulating policies for improving the quality of education and sustainable professional development of lecturers.]]>
