Cyberbullying on social media platforms, particularly X (formerly Twitter), has become a serious issue that negatively affects users' mental health and well-being. Automatic cyberbullying detection in Indonesian remains challenging due to the widespread use of informal language, slang, abbreviations, and highly imbalanced class distributions. This study proposes a hybrid deep learning model that integrates IndoBERTweet with a Bidirectional Gated Recurrent Unit (BiGRU) to improve cyberbullying detection performance on Indonesian tweets. A dataset of Indonesian tweets was collected from X and annotated using a multi-stage dual large language model (LLM) labeling strategy to reduce the time and effort required for manual annotation while maintaining label consistency. To address class imbalance, this study investigates the effectiveness of Focal Loss and label distribution modification through multiple experimental scenarios. The proposed approach was evaluated using accuracy, precision, recall, and F1-score. The best performance was achieved by combining Focal Loss with a modified four-class label configuration consisting of Rude and Vulgar Words, Sexual Harassment, Body Shaming and Hate Speech, and Non-Cyberbullying. This configuration obtained an accuracy of 0.93, precision of 0.90, recall of 0.90, and F1-score of 0.90. These findings demonstrate that integrating contextual language representations with sequential modeling, supported by an efficient LLM-assisted labeling strategy and class imbalance handling, provides an effective approach for Indonesian cyberbullying detection and offers a practical solution for large-scale social media content moderation.
F. Nafiah, Aviolla Terza Damaliana, K. M. Hindrayani· JOURNAL OF APPLIED INFORMATI...· 0 citations
Inflation forecasting remains a complex problem due to nonlinear dynamics and interactions among macroeconomic variables, particularly in emerging economies such as Indonesia. Previous studies using deep learning models, including Long Short-Term Memory (LSTM), have shown promising results but often suffer from high variance and limited robustness, especially when temporal dependencies are not properly preserved. This study aims to develop a more stable and accurate forecasting model by integrating Bagging with a Stacked LSTM architecture using the Moving Block Bootstrap (MBB) method. The proposed model utilizes multivariate time series data consisting of inflation, exchange rate (USD/IDR), BI interest rate, and money supply, with preprocessing techniques including Z-score normalization and sliding window transformation. Experimental results show that the model achieves an RMSE of 0.4273 and MAE of 0.3048, indicating good predictive performance. Compared to baseline models such as ARIMA and single LSTM, the proposed approach provides more stable and consistent forecasting results. The model is also able to generate reliable predictions for the next 12 periods, demonstrating its ability to capture temporal patterns effectively. These findings suggest that the integration of Bagging, Stacked LSTM, and MBB improves model robustness and forecasting accuracy. The proposed approach can support data-driven decision-making in economic policy, although further research is needed to incorporate additional variables and explore more advanced forecasting architectures.
