Intelligent News Aggregation System with Automatic Classification, Clustering, and Summarization

The rapid growth of online news content has made it increasingly difficult for users to access relevant information efficiently. This study presents the development of an intelligent web-based news aggregation system that performs automatic classification, clustering, and summarization of Indonesian-language news articles. The system aims to enhance the news reading experience by organizing articles by category and topic, and by providing concise summaries. The system was built using the ADDIE development model, with each AI component trained and evaluated separately. News classification is handled by a BLSTM-2DCNN model trained on the IndoSum dataset, achieving 86% accuracy and an F1-score of 0.85. This model was also applied to classify 37,187 real-world articles scraped from Kompas and TribunNews during June 2025. Topic clustering is performed using K-means with entropy-weighted Bag-of-Words features over 5-day sliding windows. The clustering quality, evaluated using the Calinski-Harabasz Index, ranged from 5.21 to 525.44 with an average of 80.53, indicating varying cluster cohesion. For summarization, a fine-tuned BART model was used to summarize the article closest to each cluster’s centroid. The model achieved ROUGE scores of 0.6389 (ROUGE-1), 0.5458 (ROUGE-2), and 0.6017 (ROUGE-L). The integrated system automatically scrapes news, classifies and clusters articles, and displays generated summaries through a user-friendly web interface. The results show that combining deep learning and natural language processing offers an effective approach for intelligent news aggregation, helping users consume news faster and more meaningfully.