<![CDATA[Grade Point Average (GPA) is one of the primary indicators used to measure students' academic achievement during their university education. GPA information enables study programs to evaluate academic performance, monitor learning progress, and develop strategies to improve educational quality. This study aims to analyze the GPA achievement of students in the Informatics Management Study Program at LP3I Jakarta Polytechnic using the Decision Tree C4.5 algorithm. The dataset consists of students' academic records, where Semester Grade Point Average (SGPA) scores from the first to the final semester serve as predictor attributes, while GPA achievement categories function as the target variable. The research process includes data collection, preprocessing, attribute selection, data transformation, and classification model development using RapidMiner. The Decision Tree C4.5 algorithm was selected because it generates interpretable decision tree models and identifies the most influential attributes affecting GPA achievement. The resulting classification rules are expected to reveal the relationship between semester GPA performance and final GPA categories. The findings can assist study programs in identifying students with different academic performance levels at an early stage, supporting data-driven academic decision-making, and developing strategies to improve learning outcomes and overall educational quality at LP3I Jakarta Polytechnic.]]>
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