Klasifikasi Kesehatan Mental Menggunakan Support Vector Machine Berdasarkan Screen Time dan Interaksi Sosial Digital - Pendi Pendi; Heni Sulistiani (Forum Kerjasama Pendidikan Tinggi, 2026)
Mental health is an important aspect that influences the quality of life of individuals, especially in adolescents and young adults who are vulnerable to stress due to the increased use of digital devices. Technological developments have led to increased screen time and the intensity of digital social interactions, which have the potential to affect mentsal health conditions. This study aims to develop a mental health classification model using the Support Vector Machine (SVM) method with a Radial Basis Function (RBF) kernel based on digital behavior data, including daily device usage time, social media time, number of positive interactions, and number of negative interactions. The dataset used is secondary data obtained from Kaggle and goes through the stages of pre-processing, feature selection, data normalization, and division of training and test data with a ratio of 80:20. The built SVM model is able to classify mental health conditions into three classes, namely Healthy, Stressed, and Risky. The evaluation results show that the accuracy of the resulting model is 94.3%, with a precision value of 66.3%, a recall of 96.1%, and an f1-score of 74.1%. These results indicate that the variables of screen time and digital social interaction have strong potential to be used as a basis for objective and data-based mental health classification.