Implementation of AI in Student Health Risk Analysis: Case Study Using Random Forest and SVM Algorithms - Daulay, Parhan; Ridho, Muhammad; Hidayat, Muhammad Ferdiansyah; Dewi, Sri; Ramadhani, Fanny (QISTINA: Jurnal Multidisiplin Indonesia, 2024)
The health of students on campus is often threatened by stress and unhealthy living habits, such as poor sleep patterns or lack of physical activity. Students' health is important to their achievement and well-being. By using AI and machine learning algorithms such as Random Forest and Support Vector Machine (SVM), we can analyze health data more quickly and accurately. This helps detect problems early and provide timely assistance. Early identification of students who have the potential to experience health problems, so that timely intervention can be carried out. This research uses quantitative methods by collecting from various sources related to the use of AI and machine learning algorithms in analyzing student health at Medan State University. Data was collected through a survey at Medan State University, covering daily habits such as sleep patterns, physical activity and stress levels. With the results of this analysis, campuses can design health policies that are more effective and appropriate to student needs. It is hoped that this research can contribute to improving the health and well-being of students in the campus environment, as well as preventing more serious health problems in the future.