Analysis of Student Graduation Classification Using the Decision Tree Method

Student graduation is one of the important indicators in assessing the success of the academic process in higher education. Low graduation rates can have an impact on the quality of institutions and the effectiveness of the education system. Therefore, an analysis method is needed that is able to accurately classify student graduation status based on available academic data. This study aims to analyze the graduation classification of students using the Decision Tree method with the help of the RapidMiner application. The data used is in the form of student academic data that has gone through the pre-processing stage, including data cleaning and transformation. The Decision Tree method was chosen because it produces a classification model that is easy to understand through clear decision rules. Model evaluation is carried out using a confusion matrix to measure classification performance. The results of the study show that the Decision Tree method is able to classify student graduation with a good level of accuracy and identify attributes that affect graduation. This research is expected to be the basis for decision-making for academics in an effort to increase student graduation rates.