K-MEANS CLUSTERING USING ELBOW METHOD IN CASE OF DIABETES MILLETUS TYPE II IN INDONESIA
DOI:
https://doi.org/10.31258/jsmds.v1i2.5Keywords:
clustering, diabetes MiletusAbstract
Indonesia is ranked 7th out of 10 countries with the
highest number of sufferers. BPJS Health include First Level
Health Facilities (FKTP) and Advanced Referral Health
Facilities (FKRTL) which Type 2 diabetes mellitus is one of
the ten most common diagnoses at FKRTL visits and ranks
third after follow-up examinations after treatment for
conditions other than malignant neoplasms and kidney failure
with a percentage of 3.54% and a total of 62,455 for 2019 to
2020. In deciding policies related to the funding of BPJS
participants who suffer from diabetes mellitus, it is necessary
to have the characteristics of each region so that policy making
is more appropriate. The method used in this study uses
clustering analysis using the K-means algorithm for type
II diabetes mellitus in Indonesia from 2015-2020 by
province. Based on the outcome of the clustering
provinces in indonesia using K-Means algorithm with
optimization of the determination of the number of
cluster using elbow method formed 3 cluster. 1 st cluster has
21 province, the 2 nd cluster has 9 province and the 3 rd cluster
has 4 province.
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