Article Per Year (5 Year)
p-Index From 2019 - 2024
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Dimas Prenky Dicky Irawan
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
Journal : Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer

 1 
Klasifikasi Risiko Gagal Ginjal Kronis Menggunakan Extreme Learning Machine Dimas Prenky Dicky Irawan; Imam Cholisoddin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.942 KB)

Abstract

Kidney is an organ in humans that have a very important role in the process of managing fluid and electrolyte needs. Chronic renal failure is a disease of kidney that occurs due to kidney infection and the existence of blockage due to kidney stones. To perform the classification of chronic renal failure medical personnel are still not maximally in handling it, to deal with this problem researchers use the Extreme Learning Machine to perform the classification of chronic renal failure. The Extreme Learning Machine is a classification algorithm in which this algorithm is part of a neural network that has a good learning speed and also according to existing research results in a good accuracy value when compared to using other algorithms. This study obtained a comparison of the value of training data as well as the optimal test data with a 70:30 ratio value, many hidden layer neurons of 10 and using the bipolar sigmoid activation function of these parameters resulted in an accuracy of 99.13%. From the results of accuracy obtained, indicating that the method of Extreme Learning Machine is good enough to be used for the process of classification of chronic renal failure.
Co-Authors Edy Santoso Imam Cholisoddin