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
Tryse Rezza Biantong
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 Metode Support Vector Machine Untuk Klasifikasi Jenis Penyakit Malaria Tryse Rezza Biantong; Muhammad Tanzil Furqon; Arief Andy Soebroto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (940.595 KB)

Abstract

Malaria is a disease transmitted by female Anopheles mosquitoes infected by a parasite (protozoa) originating from the genus Plasmodium. There are four species of protozoa parasites that commonly attack humans, including: Plasmodium vivax which causes malaria tertiana, Plasmodium falciparum causes malaria tropica, Plasmodium malariae causes malaria quartana, and Plasmodium ovale causes malaria ovale. These four malaria cases almost have the same symptoms, so it is not easy to distinguish between one to another. Therefore, a system that can classify these types of malaria based on the symptoms is needed. Classification is the creation of a model that is used to classify an object into a predetermined class based on the same characteristics. One of the classification method is Support Vector Machine (SVM). Therefore the SVMs classification algorithm using the RBF kernel is being used in this study. The data used were 200 data taken from Dinas Kesehatan Kabupaten Nabire, Papua. In this test used K-fold Cross Validation with the K-fold values = 10. The best accuracy results generated by this system is 72.5% with the value of the parameter λ=0.1, σ=1, γ=0.001, C=0.1, ε=1.10-5, itermax=50 data on the ratio of 80% training data : 20% testing data.
Co-Authors Arief Andy Soebroto Muhammad Tanzil Furqon