Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
William Muris Parsaoran Nainggolan
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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

Found 1 Documents
Search

 1 
Sistem Pakar Diagnosis Penyakit Infeksi Saluran Pernapasan Akut (ISPA) Menggunakan Metode Fuzzy Tsukamoto William Muris Parsaoran Nainggolan; Edy Santoso; Nurul Hidayat
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.257 KB)

Abstract

Acute Respiratory Infection (ARI) is a major cause of morbidity (number of diseases) from various infectious diseases in the world. Bacteria, mycoplasma virus, fungi are a small part of various causes of ARI disease. Based on the severity, ARI disease can also be divided into 2, namely mild ARI and severe ARI. The low level of public information about knowledge about diseases, especially ARI, is an important factor why the mortality rate due to ARI is very high. In addition, sufficient numbers of medical personnel are needed to help communities in certain areas of Indonesia to be able to cope with ARI disease and get to know more widely ARI disease. Therefore, expert systems are expected to be a solution or rapid treatment and in helping diagnose ARI. This study also uses the Tsukamoto fuzzy method in the process of calculation and implementation. The application of fuzzy tsukamoto method of diagnosis of ARI disease, applying input from symptoms of ARI that appear as a reference for the diagnosis of disease. In the system, experts provide 5 symptoms and 2 types of ARI disease. Accuracy testing is done by testing the accuracy of 60 training data obtained from experts and there are 53 of the 60 data that match the results released by the system with expert results. The accuracy value obtained is 88.33%.
Co-Authors Edy Santoso Nurul Hidayat