Prediksi Tingkat Stres Mahasiswa Berdasarkan Data Akademik dan Tugas Perkuliahan Menggunakan Algoritma Naïve Bayes - Bella Intan (Universitas Multi Data Palembang, 2026)
Academic stress is one of the most common challenges faced by university students due to academic demands, assignment workloads, and various psychological and environmental factors. This study aims to develop a model for predicting student stress levels using the Naïve Bayes algorithm based on psychological, health, and environmental data. The dataset consists of 1,100 records with 21 attributes, including anxiety level, self-esteem, depression, sleep quality, study load, social support, and other factors related to student stress. The research methodology includes data collection, data preprocessing, descriptive statistical analysis, data splitting into training and testing sets, and classification model development using the Naïve Bayes algorithm.