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JISKa (Jurnal Informatika Sunan Kalijaga)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 25275836     EISSN : 25280074     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Library & Information Science
JISKa (Jurnal Informatika Sunan Kalijaga) adalah jurnal yang mencoba untuk mempelajari dan mengembangkan konsep Integrasi dan Interkoneksi Agama dan Informatika yang diterbitkan oleh Departemen Teknik Informasi UIN Sunan Kalijaga Yogyakarta. JISKa menyediakan forum bagi para dosen, peneliti, mahasiswa dan praktisi untuk menerbitkan artikel penelitiannya, mengkaji artikel dari para kontributor, dan teknologi baru yang berkaitan dengan informatika dari berbagai disiplin ilmu
Arjuna Subject : -
Articles 241 Documents
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Peningkatan Akurasi Temu Kembali Pengetahuan pada Kasus Sakit Kepala Menggunakan Integrasi Case-Based Reasoning dan Natural Language Processing Agus Mulyanto
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6189

Abstract

Headache disorders represent a significant global neurological challenge, yet medical diagnosis is often hindered by subjective patient complaints and limited facilities. This study proposes an integrated model combining Natural Language Processing (NLP) and Case-Based Reasoning (CBR) to enhance the accuracy of medical decision-making for headache cases. The model utilizes the Random Forest algorithm for NLP classification and Cosine Similarity within the CBR framework to identify case relevance. The dataset consists of medical records for two types of headaches: Cluster headache (G44.0) and Tension-type headache (G44.2). Experimental results demonstrate that data augmentation significantly improves model performance, increasing accuracy from 62% to 69%. For the G44.0 label, the model achieved a precision of 0.84, while the G44.2 label reached a recall of 0.87. Furthermore, the CBR system strengthens the diagnosis with a similarity level of up to 0.82 and continuous learning capabilities through the Retain stage. This integration effectively provides faster, more targeted diagnostic recommendations for medical professionals.

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