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Implementation of the Random Forest Algorithm for Classifying University Students' Stress Levels Based on DASS-21 Questionnaire Data

Implementasi Random Forest untuk Klasifikasi Tingkat Stres Mahasiswa Berdasarkan Data Kuesioner DASS-21

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DOI:

https://doi.org/10.21070/ups.11471

Keywords:

Random Forest, classification, student stress, DASS-21, machine learning

Abstract

Stress is a common psychological problem among students caused by academic pressure, achievement demands, and personal and social factors. Rapid and accurate identification of stress levels is essential to support timely interventions, yet manual assessment is often time-consuming and prone to errors. This study implements the Random Forest algorithm to classify student stress levels using DASS-21 questionnaire data. The dataset consists of 1,000 respondents and underwent preprocessing, including cleaning, scoring, labeling, and encoding. The data were divided into training and testing sets using an 80:20 ratio. Model training was performed using the Random Forest algorithm with hyperparameter optimization through GridSearchCV. The optimal parameters were n_estimators = 200, max_depth = 10, min_samples_split = 2, and min_samples_leaf = 1. The model achieved an accuracy of 73.50%, precision of 74.17%, recall of 73.50%, and an F1-score of 73.61%, demonstrating reliable performance in classifying student stress levels.

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