Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Hayyu Ilham Wicaksono
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Information Retrieval for Early Detection of Disease Using Semantic Similarity Aszani Aszani; Hayyu Ilham Wicaksono; Uffi Nadzima; Lukman Heryawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.80077

Abstract

 The growth of medical records continues to increase and needs to be used to improve doctors' performance in diagnosing a disease. A retrieval method returns proposed information to provide diagnostic recommendations based on symptoms from medical record datasets by applying the TF-IDF and cosine similarity methods. The challenge in this study was that the symptoms in the medical record dataset were dirty data obtained from patients who were not familiar with biological terms. Therefore, the symptoms were matched in the medical record data with the symptom terms used in the system and from the results, data augmentation was carried out to increase the amount of data up to about 3 times more. In the TF-IDF the highest accuracy with  is only , while after augmentation of the test data, the accuracy becomes . The highest accuracy results with the same  value using the cosine similarity method is  and with the augmented test data accuracy increasing to . From this study it was concluded that a system with sufficient and relevant input of symptoms would provide a more accurate disease prediction. Prediction results using the TF-IDF method with  are more accurate than predictions using the cosine similarity method.
Co-Authors Aszani Aszani Lukman Heryawan Uffi Nadzima