<![CDATA[Students frequently face academic pressure that impacts their learning performance and mental health. The process of determining student stress levels at Universitas Pamulang is still carried out conventionally and is not yet integrated, resulting in a time-consuming process and subjective conclusions. This research focuses on the design and implementation of an Android-based student stress level prediction system to solve these problems. The main method applied in the system development is the Naïve Bayes algorithm with the Laplace Smoothing technique to resolve zero probability values. The questionnaire consists of 15 questions and has been validated by a psychology expert. The functional reliability and internal structure of the software are validated using Black Box and White Box testing, whereas the predictive performance validity of the model is evaluated separately using a confusion matrix against 60 test samples (comprising 5 low, 32 moderate, and 23 high-risk samples). The evaluation results indicate that the system is able to classify stress levels with an overall accuracy of 85% (with class-wise F1-scores of 0.50 for low, 0.87 for moderate, and 0.88 for high categories), although the relatively small sample size limits the generalization of the findings to a broader population. Through this application, students can independently monitor their psychological condition as a preventive measure to manage stress and support their academic success in the university environment.]]>
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