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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH International Journal of Artificial Intelligence in Medical Issues
Ahmad Naswin
Universitas Megarezky Makassar
Computer Science & IT Dentistry Economics, Econometrics & Finance Education Health Professions Medicine & Pharmacology Public Health Social Sciences

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EKSTRAKSI DATA TERSTRUKTUR PADA HALAMAN WEB MENGGUNAKAN MOBILE AGENT BERBASIS ARSITEKTUR BDI Ahmad Naswin; Sulkifli; Sitti Mawaddah Umar
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/zsd3hv83

Abstract

Abstract: The growth of semi-structured and unstructured data on web pages has increased the need for efficient, autonomous, and structure-aware methods for structured data extraction. This study proposes a mobile agent approach based on the BDI (Belief-Desire-Intention) architecture as an alternative to conventional wrappers for structured web data extraction. The system was developed using the JADE (Java Agent Development Framework) platform and the Prometheus methodology, involving two main agents: AgenDownload, responsible for migration and retrieval of web page content, and AgenEkstraksi, which implements the Simple Tree Matching and MDR (Mining Data Regions) algorithms to identify and extract flat data records. Experiments were conducted on 40 web pages from Indonesian and English online bookstores. The results show an average recall of 73%, indicating the system capability to locate most target records, while the average precision of 15.59% indicates that further refinement is needed in post-extraction filtering. The mobile agent approach remains relevant in distributed network scenarios with bandwidth constraints, near-source processing requirements, and autonomous operation needs. This study contributes a multi-role agent system architecture that can be adapted for automated web data extraction.   Keywords: web data extraction; mobile agent; BDI architecture; Simple Tree Matching; JADE   Abstrak: Pertumbuhan data semi-terstruktur dan tidak terstruktur pada halaman web meningkatkan kebutuhan terhadap metode ekstraksi data terstruktur yang efisien, otonom, dan dapat beradaptasi terhadap variasi struktur HTML. Penelitian ini mengajukan pendekatan berbasis mobile agent dengan arsitektur BDI (Belief-Desire-Intention) sebagai alternatif terhadap wrapper konvensional dalam proses ekstraksi data terstruktur pada halaman web. Sistem dibangun menggunakan platform JADE (Java Agent Development Framework) dan metodologi Prometheus, melibatkan dua agen utama: AgenDownload yang bertugas melakukan migrasi dan pengambilan konten halaman web, serta AgenEkstraksi yang mengimplementasikan algoritma Simple Tree Matching dan MDR (Mining Data Regions) untuk mengidentifikasi serta mengekstrak flat data records. Pengujian dilakukan terhadap 40 halaman web toko buku daring berbahasa Indonesia dan Inggris. Hasil menunjukkan rata-rata recall sebesar 73%, yang mengindikasikan kemampuan sistem dalam menemukan sebagian besar target data, sedangkan rata-rata precision sebesar 15,59% menunjukkan perlunya penyempurnaan mekanisme pemfilteran hasil ekstraksi. Pendekatan mobile agent tetap relevan pada skenario jaringan terdistribusi dengan keterbatasan bandwidth, kebutuhan pemrosesan dekat sumber data, dan operasi otonom. Kontribusi penelitian ini adalah rancangan arsitektur sistem agen multi peran yang dapat diadaptasi untuk kebutuhan ekstraksi data web secara otomatis.   Kata Kunci: ekstraksi data web; mobile agent; arsitektur BDI; Simple Tree Matching; JADE  
Machine Learning-Based Prediction of HIV/AIDS Infection and Treatment Effectiveness: A Clinical Dataset Analysis Agus Aan Jiwa Permana; I Gusti Ngurah Wikranta Arsa; Ahmad Naswin; Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.362

Abstract

The early and accurate prediction of HIV/AIDS infection is critical to improving clinical decision-making and ensuring effective patient management. This study presents a comprehensive machine learning-based approach to predict HIV/AIDS infection status and evaluate the effectiveness of antiretroviral treatments using a well-documented clinical dataset from 1996, comprising 2,139 patient records and 34 features. Through rigorous preprocessing, exploratory data analysis, and feature engineering, several new clinically relevant attributes were constructed, such as CD4/CD8 ratios and immunological change metrics. Four machine learning models—Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting—were trained and evaluated. Among these, the Gradient Boosting classifier achieved the highest ROC-AUC score of 0.9335, while Random Forest provided strong predictive performance with a ROC-AUC of 0.9180 and was selected for further evaluation due to its model transparency. Key features influencing infection prediction included CD4+ and CD8+ dynamics, baseline immunological levels, and treatment history. Additionally, the study examined treatment effectiveness by analyzing CD4+ cell count responses across different therapy types. The combination of ZDV and ddI emerged as the most effective regimen, improving immune outcomes and lowering infection rates, while ZDV monotherapy showed the least favorable results. This work underscores the potential of machine learning as a clinical decision support tool in HIV/AIDS care and provides data-driven insights into treatment optimization. Future studies should incorporate longitudinal patient data and real-world clinical environments for broader applicability.
Co-Authors Agus Aan Jiwa Permana I Gusti Ngurah Wikranta Arsa Sitti Mawaddah Umar Sulkifli Sumiyatun