<![CDATA[Stress is a phenomenon experienced by all individuals as a natural response to pressure, which can impact mental and physical health. In an academic setting, the stress experienced by students is known as academic stress, which can affect their performance and mental well-being. Therefore, there is a need for effective prediction methods to aid in the management and prevention of academic stress. Therefore, there is a need to predict the level of academic stress to aid more effective management and prevention. This study uses a public dataset categorized based on the Student-life Stress Inventory (SSI), which includes psychological, physiological, social, environmental, and academic factors. Data mining is often used to detect diseases, one of which is the Random Forest algorithm. The Random Forest algorithm is applied as a classification technique for academic stress levels, with optimization using the Backward Elimination method for feature selection to improve model accuracy. The results showed that the accuracy of the Random Forest algorithm without feature selection obtained an accuracy of 86%, compared to the random forest algorithm with feature selection using the Backward Elimination method obtained a higher accuracy of 88%. This increase shows that the feature selection method can optimize model performance by selecting more relevant features. Thus, this research is expected to contribute to the management of student academic stress against the risk of academic stress.]]>
