News headline classification plays an important role in identifying event-related information from large volumes of online news. However, performance often declines when datasets are imbalanced, as non-event news dominates and causes models to favor the majority class, reducing minority detection accuracy. This study applied the IndoBERT model with two fine-tuning approaches, cross entropy loss and focal loss to classify Indonesian news headlines into event and non-event categories, aiming to evaluate the effectiveness of focal loss on imbalanced data. Using a supervised learning framework, the dataset was split into training, validation, and testing sets, and both models were trained with identical hyperparameters: learning rate 2×10⁻⁵, batch size 16, and 3 epochs. Evaluation employed accuracy, precision, recall, F1-score, confusion matrix, and ROC analysis. Results showed that cross entropy achieved 84% accuracy but lower minority-class recall, while focal loss improved performance to 95% accuracy with more balanced precision and recall. The findings indicate that focal loss helps the model focus on difficult samples, enhancing robustness and minority-class detection in imbalanced Indonesian news headline classification