ABSTRACT
COMPARATIVE ANALYSIS OF DECISION TREE, LOGISTIC
REGRESSION, AND RANDOM FOREST ALGORITHMS IN DATA
SCIENCE FOR STUDENT MENTAL HEALTH CLASSIFICATION
USING PYTHON
By
Ahmad Syafei Nursuwanda
Informatics Engineering
In Indonesia, 6.1 percent of the population aged 15 years and above
experience mental health disorders, including 12.69 percent of college students who
are vulnerable due to unstable mental conditions and life pressures. Mental health
is important for college students as it has a direct impact on their academic success.
This research uses Decision tree, Random forest, and Logistic regression
algorithms to analyze college students' mental health data using Python. The results
showed that Logistic regression had the highest accuracy of 90%, followed by
Decision tree and Random forest with 80% accuracy. The Logistic regression model
also provides the most accurate prediction of anxiety based on TMAS, with 90%
accuracy, compared to 80% from Decision tree and Random forest.
Keywords: Mental Health; Python; Classification; Algoritm; Data Science.