Integration of BERT and LSTM for Predicting Cybersecurity Service Trends Based on LinkedIn Data - Mohamad Firdaus; Yasep Azzery; Dimaz Arno Prasetio (Sultan Publisher, 2025)
The analysis and prediction of evolving cybersecurity service demands are constrained by existing methodologies, which are either semantically shallow (keyword-based TF-IDF) or contextually limited (standalone LSTM time-series models that overlook textual meaning). To bridge this scientific gap, this study develops and validates an integrated artificial intelligence framework combining Bidirectional Encoder Representations from Transformers (BERT) for deep semantic analysis and Long Short-Term Memory (LSTM) for sequential trend prediction. This pipeline is applied to a large-scale corpus of cybersecurity job descriptions collected from LinkedIn, serving as a proxy for real-world market intelligence. The methodology utilizes BERT embeddings (768-dimensional) for nuanced feature extraction, which are then combined with pseudo-temporal segmented data (proxy timeline) to enable sequential forecasting via the LSTM component. Experimental results confirm the model's robustness, the BERT component achieved 89% classification accuracy (87% precision, 88% recall) in service categorization, significantly outperforming baseline methods such as TF-IDF (which typically achieve below 75% accuracy). The LSTM component demonstrated strong predictive capability for trend forecasting, achieving a Root Mean Squared Error (RMSE) of 0.12. These findings validate the technical viability of the unified BERT-LSTM architecture for capturing both contextual and sequential patterns in professional data. The output provides organizations with objective, data-driven insights for strategic planning, thereby enhancing organizational resilience and market competitiveness in dynamic environments, particularly relevant for the Indonesian cybersecurity market.