Penerapan SMOTE dan Regresi Logistik Pada Website Skrining Awal Kesehatan Mental Mahasiswa - Wijaya, Vannes; Rachmat, Nur (Program Studi Teknik Informatika Universitas Multi Data Palembang, 2025)
Mental health is a very important aspect in realizing overall health. Students are an age group that experiences a transition from adolescence to adulthood, students tend to experience stress, especially those originating from the academic process. In this study, a website-based questionnaire system was developed to predict mental health profiles consisting of optimal mental health profiles (+-), maximum mental health profiles (++), minimal mental health profiles (–), and minimal mental health profiles (-+). The questionnaire questions and grouping of mental health profiles used the SKM-12 mental health measurement tool. The dataset used was obtained from 78 students at Multi Data University Palembang. The method used in this research is Logistic Regression using the data imbalance method, namely SMOTE with parameter solver newton-cg with data division, 70% training data and 30% test data. The results obtained in this study using confusion matrix model evaluation obtained an accuracy of 89.28% and model evaluation using K-fold cross validation obtained an accuracy of 87.43% for training data and 82.66% for test data.