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All Journal Brilliance: Research of Artificial Intelligence ARMADA : Jurnal Penelitian Multidisiplin
Asep Wahyudin
Universitas Pendidikan Indonesia, Indonesia
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Intelligent News Aggregation System with Automatic Classification, Clustering, and Summarization Ihsan Ghozi Zulfikar; Yudi Wibisono; Asep Wahyudin
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6712

Abstract

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.
Implementation of the Experiential Learning Model in Adventure Game-Based Learning Media to Improve Logical Thinking Ghina Firdha Nabila; Asep Wahyudin; Eki Nugraha
ARMADA : Jurnal Penelitian Multidisiplin Vol. 4 No. 8 (2026): ARMADA : Jurnal Penelitian Multidisplin, Agustus 2026
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

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Abstract

Algorithm and programming learning media are commonly developed as quizzes or puzzles that emphasise content delivery rather than meaningful learning experiences. This study aims to design and evaluate an adventure game-based learning medium integrating the Experiential Learning model to improve students’ logical thinking skills in algorithm and programming instruction. The study employed Research and Development (R&D) using the ADDIE model, comprising analysis, design, development, implementation, and evaluation. The trial involved 32 Year 11 students in a one-group pre-test post-test design. Data were collected through expert validation, logical thinking tests, and student response questionnaires. The results showed that the medium achieved a high feasibility level. Students’ logical thinking skills improved significantly, indicated by a paired-samples t-test with a significance value below 0.05 and a moderate N-Gain score. Students responded positively to ease of use, learning engagement, and effectiveness. These findings indicate that integrating Experiential Learning into an adventure game effectively supports meaningful learning experiences and students’ logical thinking skills.
Co-Authors Eki Nugraha Ghina Firdha Nabila Ihsan Ghozi Zulfikar Yudi Wibisono