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Jurnal
SINTA 2
Anomaly Detection of Hospital Claim Using Support Vector Regression
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 2460-7258, 1978-1520
Published 2024-01-31
Publisher IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language eng
Abstract
BPJS Kesehatan plays a crucial role in providing affordable access to healthcare services and reducing individual financial burdens. However, deficit issues can disrupt the sustainability of the program, making anomaly detection highly important to conduct. Previous research on unsupervised anomaly detection in BPJS Kesehatan revealed a limitation with Simple Linear Regression (SLR), which only accommodates linear relationships among independent variables and the target variable of BPJS Kesehatan claim values. Minister of Health Regulation No. 52 of 2016 identified eight influential non-linear independent variables, leading to the proposal of Support Vector Regression (SVR) to address SLR's shortcomings.Research findings demonstrate SVR's superior anomaly detection performance over SLR. Interestingly, the SVR model excels in anomaly detection but lacks in prediction. Optimal tuning of SVR hyperparameters (C=9, epsilon=90, gamma=0.009, residual anomaly definition > 0.5*RMSE for both datasets) yields impressive metrics: Accuracy=0.97, Precision=0.84, Recall=0.97, and F1-Score=0.90. The anomaly detection results are expected to greatly support the sustainability of the BPJS Kesehatan program in Indonesia.
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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 of Hospital Claim Using Support Vector Regression</dc:title>
<dc:creator>Hapsari, Luthfia Nurma</dc:creator>
<dc:creator>Rokhman, Nur</dc:creator>
<dc:subject xml:lang="en-US"></dc:subject>
<dc:subject xml:lang="en-US">Anomaly Detection; BPJS Kesehatan; Support Vector Regression; PCA</dc:subject>
<dc:description xml:lang="en-US">BPJS Kesehatan plays a crucial role in providing affordable access to healthcare services and reducing individual financial burdens. However, deficit issues can disrupt the sustainability of the program, making anomaly detection highly important to conduct. Previous research on unsupervised anomaly detection in BPJS Kesehatan revealed a limitation with Simple Linear Regression (SLR), which only accommodates linear relationships among independent variables and the target variable of BPJS Kesehatan claim values. Minister of Health Regulation No. 52 of 2016 identified eight influential non-linear independent variables, leading to the proposal of Support Vector Regression (SVR) to address SLR's shortcomings.Research findings demonstrate SVR's superior anomaly detection performance over SLR. Interestingly, the SVR model excels in anomaly detection but lacks in prediction. Optimal tuning of SVR hyperparameters (C=9, epsilon=90, gamma=0.009, residual anomaly definition > 0.5*RMSE for both datasets) yields impressive metrics: Accuracy=0.97, Precision=0.84, Recall=0.97, and F1-Score=0.90. The anomaly detection results are expected to greatly support the sustainability of the BPJS Kesehatan program in Indonesia.</dc:description>
<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2024-01-31</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/91857</dc:identifier>
<dc:identifier>10.22146/ijccs.91857</dc:identifier>
<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 1 (2024): January; 1-12</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/91857/38499</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2024 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
Hapsari, L. N., & Rokhman, N. (2024). Anomaly Detection of Hospital Claim Using Support Vector Regression. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 18(1), 1–12. https://doi.org/10.22146/ijccs.91857
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IEEE
L. N. Hapsari and N. Rokhman, “Anomaly Detection of Hospital Claim Using Support Vector Regression,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 18, no. 1, pp. 1–12, 2024, doi: 10.22146/ijccs.91857.
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