Detecting the Impact of Social Media on Users' Mental Health Using Machine Learning and XAI

This research develops a machine learning-based predictive system to detect potential depression due to social media use, and compares the performance of algorithms such as Random Forest, XGBoost, and Naïve Bayes. Survey data, including age, gender, relationship status, daily usage duration, and social media platform, were used to build the model, with accuracy, precision, recall, and F1-score evaluated. XGBoost showed the best performance with 90% accuracy and a high F1-score. The main features that affect depression prediction include duration of social media use, age, and platforms. Explainable AI (XAI) techniques with LIME increase the transparency of the model, provide relevant explanations for individuals, and strengthen confidence in the predictions. This research emphasizes the importance of transparency in model implementation in the mental health field and offers a flexible solution that can be adopted for digital applications such as chatbots or real-time mental health monitoring dashboards.   Penelitian ini mengembangkan sistem prediktif berbasis machine learning untuk mendeteksi potensi depresi akibat penggunaan media sosial, serta membandingkan kinerja algoritma seper