KLASIFIKASI TINGKAT STRES MAHASISWA BERDASARKAN AKTIVITAS HARIAN MENGGUNAKAN RANDOM FOREST

Vivi Aldani

doi:10.33365/jdmsi.v7i2.2434

Vivi Aldani Program Studi Teknik Komputer, Jurusan Teknik Komputer dan Informatika, Politeknik Negeri Medan, Medan, Sumatera Utara, Indonesia

DOI: https://doi.org/10.33365/jdmsi.v7i2.2434

Keywords: Random Forest, Stress Classification, Student Mental Health, Data Mining, Feature Importance

Abstract

Student mental health is increasingly recognized as a critical factor affecting academic performance and overall well-being. This study implements the Random Forest algorithm to classify student stress levels into three categories—Low, Medium, and High—based on daily activity survey data collected from 30 students at Politeknik Negeri Medan. The dataset comprises 13 input features covering academic pressure, lifestyle habits, gadget usage, and emotional condition, along with a self-reported stress label. Data preprocessing included label encoding of categorical responses and an 80:20 train-test split. The Random Forest model was built using 100 decision trees with the Gini Impurity criterion and sqrt max features. Evaluation on the test set of 6 samples yielded 100% accuracy with perfect precision, recall, and F1-score across all three classes. Feature importance analysis revealed that deadline pressure (0.142), academic workload (0.138), and difficulty concentrating (0.121) are the most influential predictors of student stress. The majority of respondents (66.67%) fell into the Medium stress category, underscoring the pervasiveness of academic pressure. These findings demonstrate the effectiveness of Random Forest for multi-class stress classification from survey data and highlight actionable targets for campus well-being interventions.

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