<![CDATA[The increasing prevalence of mental health disorders among students, especially depression, has become a major concern in higher education. Based on an analysis of 27,901 student data, it was found that more than 60% of respondents experienced symptoms of depression and suicidal thoughts. This research aims to develop a data visualization dashboard as a tool for early detection and monitoring of students' mental health conditions. The method used is a quantitative descriptive approach based on data exploration, utilizing secondary datasets from the Kaggle platform. Visualization is conducted through three main dashboards covering the general conditions of depression, psychosocial and academic risk factors, as well as demographic distribution and intervention strategies. The analysis results show that academic pressure, lack of sleep, and financial stress are the dominant factors. This dashboard facilitates campus stakeholders, such as counselors and lecturers, in making data-driven decisions to formulate more targeted intervention strategies. This findings emphasize the importance of integrating data-driven counseling services, early detection, and tailored intervention approaches for the most vulnerable groups of students based on age and gender.]]>
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