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Anomaly Detection in Hospital Claims Using K-Means and Linear Regression
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 2460-7258, 1978-1520
Published 2021-10-31
Publisher IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language eng
Abstract
BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim to BPJS Kesehatan. The data used is BPJS claim data for 2015-2016. While the algorithm used is a combination of K-Means algorithm and Linear Regression. For optimal clustering results, density canopy algorithm was used to determine the initial centroid.Evaluation using silhouete index resulted in value of 0.82 with number of clusters 5 and RMSE value from simple linear regression modeling of 0.49 for billing costs and 0.97 for length of stay. Based on that, there are 435 anomaly points out of 10,000 data or 4.35%. It is hoped that with the identification of these, more effective follow-up can be carried out.
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Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai,
first harvested 26 Okt 2026,
last seen 26 Okt 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en-US">Anomaly Detection in Hospital Claims Using K-Means and Linear Regression</dc:title>
<dc:creator>Prakosa, Hendri Kurniawan</dc:creator>
<dc:creator>Rokhman, Nur</dc:creator>
<dc:subject xml:lang="en-US">Public Health</dc:subject>
<dc:subject xml:lang="en-US">Detection; Anomaly; BPJS Kesehatan; K-Means; Linear Regression</dc:subject>
<dc:description xml:lang="en-US"> BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim to BPJS Kesehatan. The data used is BPJS claim data for 2015-2016. While the algorithm used is a combination of K-Means algorithm and Linear Regression. For optimal clustering results, density canopy algorithm was used to determine the initial centroid.Evaluation using silhouete index resulted in value of 0.82 with number of clusters 5 and RMSE value from simple linear regression modeling of 0.49 for billing costs and 0.97 for length of stay. Based on that, there are 435 anomaly points out of 10,000 data or 4.35%. It is hoped that with the identification of these, more effective follow-up can be carried out.</dc:description>
<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
<dc:contributor xml:lang="en-US">Master Program of Computer Science</dc:contributor>
<dc:contributor xml:lang="en-US">2Department of Computer Science and Electronics</dc:contributor>
<dc:contributor xml:lang="en-US">FMIPA UGM</dc:contributor>
<dc:date>2021-10-31</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/68160</dc:identifier>
<dc:identifier>10.22146/ijccs.68160</dc:identifier>
<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 4 (2021): October; 391-402</dc:source>
<dc:source>2460-7258</dc:source>
<dc:source>1978-1520</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/68160/32409</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
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APA 7
Prakosa, H. K., & Rokhman, N. (2021). Anomaly Detection in Hospital Claims Using K-Means and Linear Regression. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(4), 391–402. https://doi.org/10.22146/ijccs.68160
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IEEE
H. K. Prakosa and N. Rokhman, “Anomaly Detection in Hospital Claims Using K-Means and Linear Regression,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 15, no. 4, pp. 391–402, 2021, doi: 10.22146/ijccs.68160.
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