@article{Sofia_2024, title={K-MEANS CLUSTERING USING ELBOW METHOD IN CASE OF DIABETES MILLETUS TYPE II IN INDONESIA}, volume={1}, url={https://jurnalmipa.unri.ac.id/jsmds/index.php/files/article/view/5}, DOI={10.31258/jsmds.v1i2.5}, abstractNote={<p>Indonesia is ranked 7th out of 10 countries with the<br>highest number of sufferers. BPJS Health include First Level<br>Health Facilities (FKTP) and Advanced Referral Health<br>Facilities (FKRTL) which Type 2 diabetes mellitus is one of<br>the ten most common diagnoses at FKRTL visits and ranks<br>third after follow-up examinations after treatment for<br>conditions other than malignant neoplasms and kidney failure<br>with a percentage of 3.54% and a total of 62,455 for 2019 to<br>2020. In deciding policies related to the funding of BPJS<br>participants who suffer from diabetes mellitus, it is necessary<br>to have the characteristics of each region so that policy making<br>is more appropriate. The method used in this study uses<br>clustering analysis using the K-means algorithm for type<br>II diabetes mellitus in Indonesia from 2015-2020 by<br>province. Based on the outcome of the clustering<br>provinces in indonesia using K-Means algorithm with<br>optimization of the determination of the number of<br>cluster using elbow method formed 3 cluster. 1 st cluster has<br>21 province, the 2 nd cluster has 9 province and the 3 rd cluster<br>has 4 province.</p>}, number={2}, journal={Journal of Statistical Methods and Data Science }, author={Sofia, Ayu}, year={2024}, month={Apr.} }