eprintid: 9184
rev_number: 12
eprint_status: archive
userid: 2098
dir: disk0/00/00/91/84
datestamp: 2024-09-12 06:37:47
lastmod: 2024-09-12 06:37:47
status_changed: 2024-09-12 06:37:47
type: article
metadata_visibility: show
contact_email: repository@staff.ukdw.ac.id
creators_name: , Angelina Rumuy
creators_name: , Rosa Delima
creators_name: , Kuncoro Probo Saputra
creators_name: , Joko Purwadi
creators_id: 71190506
creators_id: 0228107401
creators_id: 0506098901
creators_id: 0515047001
title: APPLICATION OF THE MINKOWSKI DISTANCE SIMILARITY METHOD IN CASE-BASED REASONING FOR STROKE DIAGNOSIS
ispublished: pub
subjects: QA76
divisions: fak_tein
full_text_status: public
keywords: Case-Based Reasoning, Stroke Diagnosis, Minkowski Distance similarity
abstract: A Stroke is a cerebrovascular disease characterized by impaired brain function due to damage or death of brain tissue caused by reduced or blocked blood and oxygen flow to the brain. Expert systems can be used as learning aids for medical students to diagnose stroke. Medical records of stroke cases can be reused as a reference for diagnosing stroke when there are new cases, known as the case-based reasoning (CBR) method. This study implements the Minkowski distance similarity method in CBR to calculate the similarity value between cases, where each similar case has the same solution. This study uses the Minkowski distance similarity method in CBR to obtain the most optimal value of r and the most appropriate threshold value in the expert system for stroke diagnosis. The diagnosis process is carried out by inputting the patient's condition, symptoms, and risk factors. Then the system will calculate the similarity value and take the case with the highest similarity value as the solution, providing that the similarity value must be greater than or equal to the threshold value. Based on system testing, the best accuracy value was achieved by applying a threshold value of 75 with an r value of 3 or 4, with an accuracy rate of 88,89%, a recall value of 88%, and a precision of 100%.
date: 2023-11
publication: JUITA : Jurnal Informatika
volume: 11
number: 2
publisher: Program Studi Teknik Informatika, Universitas Muhammadiyah Purwokerto
pagerange: 323-332
id_number: DOI: 10.30595/juita.v11i2.18582
refereed: TRUE
issn: 2579-8901
official_url: https://doi.org/10.30595/juita.v11i2.18582
funders: rosadelima@staff.ukdw.ac.id
citation:   Angelina Rumuy and Rosa Delima and Kuncoro Probo Saputra and Joko Purwadi  (2023) APPLICATION OF THE MINKOWSKI DISTANCE SIMILARITY METHOD IN CASE-BASED REASONING FOR STROKE DIAGNOSIS.  JUITA : Jurnal Informatika, 11 (2).  pp. 323-332.  ISSN 2579-8901     
document_url: https://katalog.ukdw.ac.id/9184/1/Application%20of%20the%20Minkowski%20Distance%20Similarity.pdf