Detecting Depression in Indonesian Social Media: A FastText and GA-Optimized with CNN-RNN Approach

Depression has become an increasingly pervasive mental health issue in Indonesia, with many individuals expressing emotional distress through social media platforms such as X. The informal, unstructured, and context-dependent nature of social media language presents challenges for automatic detection using natural language processing (NLP). This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to identify depressive indications in Indonesian tweets. A total of 58,115 tweets were collected and manually labeled into two classes: depressed and non-depressed. The text data underwent preprocessing steps including cleaning, case folding, normalization, stopword removal, stemming, and tokenization. Features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF), and semantic relationships were enriched through FastText embeddings. The FastText model was trained on three corpora—Tweet, IndoNews, and a combined Tweet+IndoNews—resulting in a similarity corpus of 339,128 entries. Hyperparameter optimization was conducted using Genetic Algorithm (GA), tuning learning rate, layer size, and dropout rate. The best-performing configuration used three 1D convolutional layers (kernel sizes 3, 5, 7), a SimpleRNN layer with 32 units, a dropout rate of 0.6, and a learning rate of 0.001. This model achieved an accuracy of 85.54%, an increase of 2.13% from the baseline of 83.41%. The results demonstrate that integrating CNN-RNN architecture with semantic feature expansion and GA-based optimization enhances depression detection in informal social media texts. Furthermore, the proposed approach supports long-term sustainability by utilizing open-source tools and lightweight neural architectures, making it adaptable to evolving language patterns and scalable for digital mental health monitoring systems in the Indonesian context.