Page 22 - Proceeding The 2nd International Seminar of Science and Technology : Accelerating Sustainable Innovation Towards Society 5.0

nd
The 2 International Seminar of Science and Technology
“Accelerating Sustainable innovation towards Society 5.0”
ISST 2022 FST UT 2022
Universitas Terbuka
K-MEANS CLUSTERING FOR DISEASE SPREAD
AREAS DENGUE HEMORRHAGIC FEVER (DHF) IN
EAST LOMBOK NTB
1*
Yunita Wilawardani , Wiwit Pura Nurmayanti ,
1
Sausan Nisrina , Ristu Haiban Hirzi , Abdul Rahim
2
1
1
1 Department of Statistics, Faculty of Mathematics and Natural
Sciences, Universitas Hamzanwadi (INDONESIA)
2 Department of Pharmacy, Faculty of Pharmacy, Universitas
Mulawarman (INDONESIA)
*Corresponding author: wiwit.adiwinata3@gmail.com

Abstract

K-Means is one algorithm in Data Mining that is used to categorize or
cluster data. The advantages of K-Means compared to other cluster
methods are that it is easy to implement and can be scalable for large
datasets. K-Means can be applied in various fields, one of which is the
health sector, namely Dengue Haemorrhagic Fever (DHF) data. DHF
is one of the environmental health problems that increases in East
Lombok Regency, NTB, so clustering is necessary to see the spread
of DHF itself. The purpose of this study was to view the description of
DHF data and to classify the areas of DHF distribution in East Lombok.
Based on the results of the analysis, information was got that the
highest number of cases occurred in Selong District, 85 cases and the
lowest cases were in Suela and Sembalun Districts, where there were
no dengue cases. For the DHF distribution area, we got three clusters.
Cluster-1 with a high category that is 13 sub-districts, and Cluster-2
with a low category that is 8 sub-districts.
Keywords: Data Mining, K-Means, Dengue Haemorrhagic Fever
(DHF), Clustering, Lombok

ISST 2022 – FST Universitas Terbuka, Indonesia 1
International Seminar of Science and Technology “Accelerating Sustainable
Towards Society 5.0