PENERAPAN ALGORITMA RANDOM FOREST DALAM PENCARIAN POLA KLAIM BPJS RUMAH SAKIT UMUM KUMALA SIWI MIJEN KUDUS
DOI:
https://doi.org/10.34127/jrlab.v14i3.1770Keywords:
Random Forest, BPJS Claims, Hospital Management, Data MiningAbstract
The persistently high number of pending BPJS Kesehatan claims in hospitals impacts cash flow and financial risk, necessitating data-driven solutions to improve the effectiveness of claims management. This study aims to analyze the factors influencing BPJS claims status and test the performance of the Random Forest algorithm in classifying eligible and pending claims. The results show that feature analysis identified total hospital charges and LOS as the most dominant variables, followed by diagnosis and treatment codes. The Random Forest model, with 30 decision trees, achieved 96.88% accuracy with balanced precision and recall, despite imbalanced data (520 eligible claims and 24 pending). Claim patterns indicate that claims with high costs, long LOS, and complex diagnoses and treatments are at higher risk of delay. This study demonstrates Random Forest's technical superiority and managerial benefits as an early warning system, supporting data-driven decisions, and improving the efficiency of claims management and hospital cash flow.
References
Anitha, R., & Siva Sundhara Raja, D. (2018). Development of computer-aided approach for brain tumor detection using random forest classifier. International Journal of Imaging Systems and Technology, 28(1), 48–53. https://doi.org/10.1002/ima.22255
Antonius Artanto EP. (2018). Faktor-Faktor Penyebab Klaim Tertunda BPJS Kesehatan RSUD Dr. Kanujoso Djatiwibowo Periode Januari-Maret 2016 Antonius Artanto EP. Jurnal Administrasi Rumah Sakit, 4.
Ariyanti, F., & Gifari, M. T. (2019). Analisis Persetujuan Klaim BPJS Kesehatan pada Pasien Rawat Inap. Jurnal Ilmu Kesehatan Masyarakat, 8(04), 156–166. https://doi.org/10.33221/jikm.v8i04.415
Frempong, N. K., Nicholas, N., & Boateng, M. A. (2017). Decision Tree as a Predictive Modeling Tool for Auto Insurance Claims. International Journal of Statistics and Applications, 7(2), 117–120. https://doi.org/10.5923/j.statistics.20170702.07
Lan, T., Hu, H., Jiang, C., Yang, G., & Zhao, Z. (2020). A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification. Advances in Space Research, 65(8), 2052–2061. https://doi.org/10.1016/j.asr.2020.01.036
Li, Y., Yan, C., Liu, W., & Li, M. (2018). A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification. Applied Soft Computing Journal, 70, 1000–1009. https://doi.org/10.1016/j.asoc.2017.07.027
Lin, W., Wu, Z., Lin, L., Wen, A., & Li, J. (2017). An ensemble random forest algorithm for insurance big data analysis. IEEE Access, 5, 16568–16575. https://doi.org/10.1109/ACCESS.2017.2738069
Moon, M., & Lee, S. K. (2017). Applying of decision tree analysis to risk factors associated with pressure ulcers in long-term care facilities. Healthcare Informatics Research, 23(1), 43–52. https://doi.org/10.4258/hir.2017.23.1.43
Mustika Kurniawati, Tuty Satrijawati, & Tuti Hariyanto. (2020). Faktor Yang Mempengaruhi Pengembalian Administrasi Klaim Bpjs Kesehatan Ditinjau Dari Syarat-Syarat Kelengkapan Administrasi Klaim. Chmk Health Journal , Volume 4 Nomor 3.
Nuraini, N., Wijayanti, R. A., Putri, F., Alfiansyah, G., Deharja, A., & Santi, M. W. (2019). Optimalisasi Manajemen Penanganan Klaim Pending Pasien Bpjs Rawat Inap Di Rumah Sakit Citra Husada Jember. Kesmas Indonesia, 11, 24–35. http://jos.unsoed.ac.id/index.php/kesmasindo/article/view/1314/1031
Nurzaman, F. (2017). Penerapan Algoritma Regresi Linier Untuk Prediksi Jumlah Klaim Pada Asuransi Kesehatan. Seminar Nasional Teknologi Informasi, 105–114.
Nyoman, I., Semarajana, G., & Soewondo, P. (2019). Icash-A102 Factors Related To Pending Claim In Indonesian National Health Insurance (Jkn): A Systematic Review (Nomor 4).
Pal, R., & Pal, S. (t.t.). Application of Data Mining Techniques in Health Fraud Detection. International Journal of Engineering Research and General Science, 3(5). www.ijergs.org
Pal, R., & Pal, S. (2015). Application of Data Mining Techniques in Health Fraud Detection. International Journal of Engineering Research and General Science, 3(5), 129–137. www.ijergs.org
Robson, B., & Boray, S. (2019). Studies in the use of data mining, prediction algorithms, and a universal exchange and inference language in the analysis of socioeconomic health data. Computers in Biology and Medicine, 112. https://doi.org/10.1016/j.compbiomed.2019.103369
Sari, M., Gulbandila, E., & Dalkilic, N. (2018). Risk Assessment with Decision Tree in Professional Liability Insurance: In Accounting. Journal of Artificial Intelligence, 12(1), 18–23. https://doi.org/10.3923/jai.2019.18.23
Vekeman, F., Piña-Garza, J. E., Cheng, W. Y., Tuttle, E., Giguère-Duval, P., Oganisian, A., Damron, J., Sheng Duh, M., Shen, V., Saurer, T. B., Montouris, G. D., & Isojarvi, J. (2019). Development of a classifier to identify patients with probable Lennox–Gastaut syndrome in health insurance claims databases via random forest methodology. Current Medical Research and Opinion, 35(8), 1415–1420. https://doi.org/10.1080/03007995.2019.1595552
Wang, C., Wang, H., Zhou, C., & Chen, H. (2020). ExperienceThinking: Constrained hyperparameter optimization based on knowledge and pruning. Knowledge-Based Systems, 106602. https://doi.org/10.1016/j.knosys.2020.106602
Zhan, C., Zheng, Y., Zhang, H., & Wen, Q. (2021). Random-Forest-Bagging Broad Learning System with Applications for COVID-19 Pandemic. IEEE Internet of Things Journal, 8(21), 15906–15918. https://doi.org/10.1109/JIOT.2021.3066575
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