Open AccessPenggunaan Algoritma Nearest Neighbor Pada Sistem Penalaran Berbasis Kasus Untuk Diagnosis Penyakit ISPA
Author(s) -
Miswar Papuangan,
Munazat Salmin
Publication year - 2020
Publication title -
jurnal serambi engineering
Language(s) - English
Resource type - Journals
eISSN - 2541-1934
pISSN - 2528-3561
DOI - 10.32672/jse.v5i1.1739
Subject(s) - nose , sore throat , medicine , confidence interval , k nearest neighbors algorithm , artificial intelligence , computer science , surgery
Acute Respiratory Infection is defined as an acute respiratory disease caused by an infectious agent that is transmitted from human to human. Symptoms include shortness of breath, difficulty breathing, sore throat, fever, wheezing, runny nose, and cough. This research implements CBR to help diagnose ARI. The diagnosis process is done by entering a new problem that contains the symptoms and risk factors that will be diagnosed in the system. The normalized nearest neighbor method with expert confidence is used to calculate the similarity between new problems and cases stored on a case basis. The results of testing the system using 112 case data with 78 case stored in a case base and 34 data used as new cases data, the system has identified four types of ARI disease with a system performance measurement 97.06%.