Implementasi Algoritma Support Vector Machine untuk Prediksi Kesejahteraan Psikologis Remaja Berdasarkan Analisis Sentimen Media Sosial - Mutia Mawardah; Fatmasari Fatmasari; Susan Dian Purnamasari (Direktorat Riset dan Pengabdian Masyarakat Universitas Bina Darma Palembang, 2026)
The increasing use of social media among adolescents may influence their psychological well-being. This study aimed to implement the Support Vector Machine (SVM) algorithm to predict adolescent psychological well-being based on social media sentiment analysis. A quantitative approach was employed involving 157 social media users aged 13–18 years. Data were collected using the Social Media Sentiment Analysis Revised (ASMS-R) and Adolescent Psychological Well-Being Revised (KPD-R) instruments. The prediction model was developed using SVM with a Radial Basis Function (RBF) kernel and evaluated through a confusion matrix. The results revealed a positive relationship between social media sentiment and psychological well-being (r = 0.565). The SVM model achieved an accuracy of 65.63%. These findings indicate that social media sentiment analysis can be utilized as an indicator for predicting adolescent psychological well-being and may support the development of artificial intelligence-based early detection systems.