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Reseach Article

The Implementation of Classification Algorithm C4.5 in Determining the Illness Risk Level for Health Insurance Company in Indonesia

by Apriyudha Angkasa P., Devi Fitrianah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 37
Year of Publication: 2020
Authors: Apriyudha Angkasa P., Devi Fitrianah
10.5120/ijca2020919883

Apriyudha Angkasa P., Devi Fitrianah . The Implementation of Classification Algorithm C4.5 in Determining the Illness Risk Level for Health Insurance Company in Indonesia. International Journal of Computer Applications. 177, 37 ( Feb 2020), 44-50. DOI=10.5120/ijca2020919883

@article{ 10.5120/ijca2020919883,
author = { Apriyudha Angkasa P., Devi Fitrianah },
title = { The Implementation of Classification Algorithm C4.5 in Determining the Illness Risk Level for Health Insurance Company in Indonesia },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2020 },
volume = { 177 },
number = { 37 },
month = { Feb },
year = { 2020 },
issn = { 0975-8887 },
pages = { 44-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number37/31150-2020919883/ },
doi = { 10.5120/ijca2020919883 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:48:02.238836+05:30
%A Apriyudha Angkasa P.
%A Devi Fitrianah
%T The Implementation of Classification Algorithm C4.5 in Determining the Illness Risk Level for Health Insurance Company in Indonesia
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 37
%P 44-50
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fundamental thing on health insurance is how to manage all contributions fee from membership insurance, so it can use for finance health services. In this writer’s case, the problem of health insurance is when registered membership insurance, there's no validation or adjustment about fee insurance with a history of illness from the applicant. That thing will be increasing financial cost if insurance does not use another approach from health services like promotive and preventive services for manage illness registered membership for health insurance, so that can be suppress financing of health services. Based on data on health insurance, they can do classification processing data and combined with algorithm C 4.5 for proses classification. Classification that has been used for mapping the level of risk illness membership in health insurance. Result from this research using a ten-fold cross-validation / confusion matrix with accuracy 99,87%.

References
  1. M. V. Pauly, P. Danzon, P. Feldstein, and J. Hoff, “A Plan For ‘Responsible National Health Insurance,’” J. Med. Soc. N. J., vol. 77, no. 3, pp. 207–208, 1991.
  2. M. Geruso and T. J. Layton, “Selection in health insurance markets and its policy remedies,” J. Econ. Perspect., vol. 31, no. 4, pp. 23–50, 2017.
  3. Ministry of Health RI, Indonesia Health Profile 2017 Ministry of Health of the Republic of Indonesia 2018. 2018.
  4. C. Brotons, “Prevention and Health Promotion in Clinical Practice,” no. 4, pp. 1–3, 2015.
  5. M. Sadikin, I. Nurhaida, D. Fitrianah, A. R. Dwiyanto, Harwikarya, and M. M. Sarinanto, “IS Strategic Plan for Higher Education Based on COBIT Assessment: A Case Study,” Int. J. Inf. Educ. Technol., vol. 5, no. 8, pp. 629–633, 2015.
  6. S. S. Nikam, “A Comparative Study of Classification Techniques in Data Mining Algorithms,” Int. J. Mod. Trends Eng. Res., vol. 4, no. 7, pp. 58–63, 2015.
  7. Y. R. W. U. Joko Purnomo, Wawan Laksito, “Aplikasi Penunjang Keputusan Penerimaan,” Implementasi Algoritm. C 4.5 dalam Pembuatan Apl. Penunjang Keputusan Penerimaaan Pegawai CV. Din. Ilmu, vol. 2, 2014.
  8. M. Aryuni and E. D. Madyatmadja, “Feature selection in credit scoring model for credit card applicants in XYZ bank: A comparative study,” Int. J. Multimed. Ubiquitous Eng., vol. 10, no. 5, pp. 17–24, 2015.
  9. A. Wijaya and A. S. Girsang, “Use of Data Mining for Prediction of Costumer Loyalty,” CommIT (Communication Inf. Technol. J., vol. 10, no. 1, p. 41, 2015.
  10. D. W. T. Putra, “Algoritma C4.5 untuk Menentukan Tingkat Kelayakan Motor Bekas yang Akan Dijual,” J. TEKNOIF, vol. 4, no. 1, pp. 16–22, 2016.
  11. M. Sadikin and F. Alfiandi, “Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk,” Int. J. Electr. Comput. Eng., vol. 8, no. 6, p. 4763, 2018.
  12. S. A. Lusinia, “Algoritma C4.5 dalam Menganalisa Kelayakan Kredit (Studi kasus di Koperasi Pegawai Republik Indonesia (KP-RI) Lengayang Pesisir Selatan, Painan, Sumatera Barat),” Issn 2356-0010, vol. 1, no. 2, pp. 6–10, 2014.
  13. L. N. Rani, “Klasifikasi Nasabah Menggunakan Algoritma C4.5 Sebagai Dasar Pemberian Kredit,” J. KomTekInfo Fak. Ilmu Komput., vol. 2, no. 2, pp. 33–38, 2016.
  14. H. Widayu, S. Darma, N. Silalahi, and Mesran, “Data Mining Untuk Memprediksi Jenis Transaksi Nasabah Pada Koperasi Simpan Pinjam Dengan Algoritma C4.5,” Issn 2548-8368, vol. Vol 1, No, no. June, p. 7, 2017.
  15. Y. Andrian and M. R. Wahaydi, “Analisis Kinerja Data Mining Algoritma kampus ABC jurnal 12,” 2012.
  16. M. F. Arifin and D. Fitrianah, “Rekomendasi Penerimaan Mitra Penjualan Studi Kasus : PT Atria Artha Persada,” no. January 2018, 2018.
  17. W. Wiharto, H. Kusnanto, and H. Herianto, “Interpretation of clinical data based on C4.5 algorithm for the diagnosis of coronary heart disease,” Healthc. Inform. Res., vol. 22, no. 3, pp. 186–195, 2016.
  18. S. Sharma, J. Agrawal, and S. Sharma, “Classification Through Machine Learning Technique: C4. 5 Algorithm based on Various Entropies,” Int. J. Comput. Appl., vol. 82, no. 16, pp. 28–32, 2013.
  19. P. Gulati, A. Sharma, and M. Gupta, “Theoretical Study of Decision Tree Algorithms to Identify Pivotal Factors for Performance Improvement: A Review,” Int. J. Comput. Appl., vol. 141, no. 14, pp. 19–25, 2016.
  20. M. G. Mohiuddin and P. Premchand, “Performance Analysis of Decision Tree Classifiers,” Int. J. Comput. Sci. Trends Technol., vol. 2, no. 2, pp. 62–70, 2014.
  21. N. Iriadi and N. Nuraeni, “Kajian Penerapan Metode Klasifikasi Data Mining Algoritma C 4.5 untuk Prediksi Kelayakan Kredit Pada Bank Mayapada Jakarta,” J. Tek. Komput. AMIK BSI, vol. II, no. 1, pp. 132–137, 2016.
  22. D. S. Kumar and K. Swati, “A Comparative Study of Various Data Transformation Techniques in Data Mining,” Int. J. Sci. Eng. Technol., vol. 4, no. 3, pp. 146–148, 2015.
  23. B. N. Patel, S. G. Prajapati, and D. K. Lakhtaria, “Efficient Classification of Data Using Decision Tree,” Bonfring Int. J. Data Min., vol. 2, no. 1, pp. 06–12, 2012.
  24. Y. Altujjar, W. Altamimi, I. Al-Turaiki, and M. Al-Razgan, “Predicting Critical Courses Affecting Students Performance: A Case Study,” Procedia Comput. Sci., vol. 82, no. March, pp. 65–71, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Algorithm C 4.5 Classification Ten Fold Cross Validation Confusion Matrix The risk Illness.