Article Per Year (5 Year)
p-Index From 2019 - 2024
0
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informasi
Putri Nur Rahayu
Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS)
Computer Science & IT

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

Found 1 Documents
Search

 1 
LEAST SQUARES SUPPORT VECTOR MACHINES PARAMETER OPTIMIZATION BASED ON IMPROVED ANT COLONY ALGORITHM FOR HEPATITIS DIAGNOSIS Nursuci Putri Husain; Nursanti Novi Arisa; Putri Nur Rahayu; Agus Zainal Arifin; Darlis Herumurti
Jurnal Ilmu Komputer dan Informasi Vol 10, No 1 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (205.767 KB) | DOI: 10.21609/jiki.v10i1.428

Abstract

Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved Ant Colony Algorithm (IACA) in determining the optimal parameters of LSSVM for diagnosing Hepatitis disease. IACA create a storage solution to keep the whole route of the ants. The solutions that have been stored were the value of the parameter LSSVM. There are three main stages in this study. Firstly, the dimension of Hepatitis dataset will be reduced by Local Fisher Discriminant Analysis (LFDA). Secondly, search the optimal parameter LSSVM with IACA optimization using the data training, And the last, classify the data testing using optimal parameters of LSSVM. Experimental results have demonstrated that the proposed method produces high accuracy value (93.7%) for  the 80-20% training-testing partition.
Co-Authors Agus Zainal Arifin Darlis Herumurti Nursanti Novi Arisa Nursuci Putri Husain