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Insight and Foresight: Mortality Trends Due to Malnutrition Haq, Mia Karisma; Zulfikar, Ihsan Ghozi; Putra, Rhisma Syahrul; Yusof, Mohamad Azfar Syazani Bin Md; Sagita, Arianti Apriani; Intanty, Della Rachmatika Noer
Jurnal Aplikasi dan Teori Ilmu Komputer Vol 7, No 2 (2024): September 2024, Jurnal Aplikasi dan Teori Ilmu Komputer (Jatikom)
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jatikom.v7i2.72389

Abstract

Malnutrition remains a significant global health problem, linked to a substantial proportion of child deaths worldwide. According to the United Nations, malnutrition is responsible for 45% of deaths in children under five. The World Food Programme estimates that over 820 million people globally suffer from hunger, with malnutrition playing a crucial role in this crisis. This study uses Python for data analysis and visualization, integrating time-series analysis and deep learning to forecast global malnutrition trends. The system processes data from 1970 to 2022, normalizes it, and trains a model comprising Conv1D and LSTM layers. The predictions are visualized using Plotly and displayed in a Flask web application, offering interactive features for exploring the data. The results highlight a notable decline in malnutrition-related deaths in both developing and developed nations, reflecting the success of previous interventions. However, developing countries continue to report a higher number of diseases and conditions associated with malnutrition, underscoring the need for further targeted interventions.
Intelligent News Aggregation System with Automatic Classification, Clustering, and Summarization Zulfikar, Ihsan Ghozi; Wibisono, Yudi; Wahyudin, Asep
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.