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
Dadang Kurniawan
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 
Implementasi Pendeteksi Penyakit Paru-Paru Berdasarkan Warna Kuku dan Suhu Tubuh Berbasis Sensor TCS3200 Dan Sensor LM35 dengan Metode Naive Bayes Dadang Kurniawan; Rizal Maulana; Mochammad Hannats Hanafi Ichsan
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 (1291.44 KB)

Abstract

The human lung is an organ that is susceptible to disease because it is in direct contact with the air inhaled through the nose. Lung medicine is currently using CT scan and sputum tests which are checked manually by sputum experts. Many people still do not know about this treatment system, and finally they are reluctant to check their lung health, because they feel inefficient and besides that the results of the tests can't go out immediately. based on these problems, there is a need for research related to the automation system to detect the severity of lung disease from patients, so when a patient comes to check their lungs, the results can be immediately known. In this study the parameters used to compare the grade level of lung disease are nail color and body temperature of patients using the Naive Bayes method. It is known that the Naive Bayes method has good accuracy and can be used based on class classification at the beginning of the process. Based on several tests carried out on the system generated TCS3200 color sensor reading error of 1.478%, and the LM35 temperature sensor reading error against the thermometer measuring instrument is 1.13%. Furthermore, testing the system using the Naive Bayes method with the number of training data as many as 24 data and test data as much as 12 data, obtained an accuracy of 91.6% with an average computing time of 0.69 seconds.
Co-Authors Mochammad Hannats Hanafi Ichsan Rizal Maulana