Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Gusti E. Yuliastuti, Adyan N. Alfiyatin, Agung M. Rizki, A. Hamdianah, H. Taufiq, W. F. Mahmudy

Abstract


According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.

Keywords


classification task; data mining; K-means; K-nearest neighbor; naïve bayes; sexually transmitted disease

Full Text:

PDF


DOI: http://doi.org/10.11591/ijece.v8i5.pp3933-3939

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).