KLASIFIKASI FAKTOR-FAKTOR PENYEBAB PENYAKIT DIABETES MELITUS DI RUMAH SAKIT UNHAS MENGGUNAKAN ALGORITMA C4.5

Authors

  • Dewi Rahma Ente Departemen Statistika, Universitas Hasanuddin, Indonesia
  • Sri Astuti Thamrin Departemen Statistika, Universitas Hasanuddin, Indonesia
  • Samsul Arifin Departemen Statistika, Universitas Hasanuddin, Indonesia
  • Hedi Kuswanto Departemen Statistika, Universitas Hasanuddin, Indonesia
  • Andreza Andreza Pendidikan Dokter, Universitas Hasanuddin, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v4i1.330

Keywords:

C4.5 algorithm, classifications, data mining, decision trees, diabetes mellitus

Abstract

Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. This study aims to identify and see the relationship between factors that influence DM disease. The method used in this research is C4.5 algorithm which is one of the algorithms used to make predictive classifications. Classification is one of the processes in data mining that aims to find patterns in relatively large data that use the representations in the form of decision trees. This method is applied to data from medical records of patients with DM in 2014-2018 taken from the Hasanuddin University Teaching Hospital. The results obtained indicate that there are four factors that influence the prediction of a patient's DM status namely; Fasting Blood Glucose (GDP), LDL Cholesterol, Triglycerides, and Body Weight.

Downloads

Download data is not yet available.

References

DeGregory, K., Kuiper, P., DeSilvio, T., Pleuss, J., Miller, R., Roginski, J., Fisher, C. B., Harness, D., Viswanath, S., Heymsfield, S. B., Dungan, I., & Thomas, D. M. (2018). A review of machine learning in obesity. Obesity Reviews, 19(5): 668–685.

Ente, D., Arifin, S., Andreza, & Thamrin, S. (2019). Comparison of C4. 5 algorithm with naive Bayesian method in classification of Diabetes Mellitus (A case study at Hasanuddin University hospital Makassar). Journal of Physics: Conference Series, 1341(9), 1–8. IOP Publishing.

Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (Vol. 12). German: Springer Science & Business Media.

Iyer, A., Jeyalatha, S., & Sumbaly, R. (2015). Diagnosis of diabetes using classification mining techniques. Journal of Data Mining & Knowledge Management Process, 5(1): 1–14.

Karegowda, A. G., Punya, V., Jayaram, M., & Manjunath, A. (2012). Rule based classification for diabetic patients using cascaded k-means and decision tree C4. 5. International Journal of Computer Applications, 45(12): 45–50.

Karisma, R. D. L. N., & Otok, B. W. (2017). Model Machine Learning CART Diabetes Melitus. Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika Dan Nilai-Nilai Islami), 1(1), 485–491.

Kurniawaty, E., & Yanita, B. (2016). Faktor-faktor yang berhubungan dengan kejadian Diabetes Melitus tipe II. Jurnal Majority, 5(2): 27–31.

Mardi, Y. (2016). Data Mining: Klasifikasi Menggunakan Algoritma C4. 5. Edik Informatika, 2(2): 213–219.

Novandya, A., & Oktria, I. (2017). Penerapan Algoritma Klasifikasi Data Mining C4. 5 Pada Dataset Cuaca Wilayah Bekasi. Jurnal Ilmiah Teknik Informatika, 6(2): 98–106.

[PERKENI] Perkumpulan Endokrinologi Indonesia. (2011). Konsensus Pengelolahan dan Pencegahan Diabetes Melitus Tipe 2 di Indonesia. Jakarta (ID): Perkumpulan Endokrinologi Indonesia.

Pramadhani, A. E., & Setiadi, T. (2014). Penerapan Data Mining untuk Klasifikasi Prediksi Penyakit ISPA (Infeksi Saluran Pernapasan Akut) dengan Algoritma Decision Tree (ID3). Jurnal Sarjana Teknik Informatika, 2(1): 831–839.

Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-Validation. In L. LIU & M. T. ÖZSU (Eds.), Encyclopedia of Database Systems (pp. 532–538). https://doi.org/10.1007/978-0-387-39940-9_565

[RISKESDAS] Riset Kesehatan Dasar. (2018). Hasil Utama RISKESDAS 2018. Retrieved June 20, 2019, from http://www.kesmas.kemkes.go.id/assets/upload/dir_519d41d8cd98f00/files/Hasil-riskesdas-2018_1274.pdf

Rodiyatul, F. S., Tama, B. A., & Mulya, M. (2010). Pengembangan Perangkat Lunak Diagnosa Penyakit Diabetes Mellitus Tipe II Berbasis Teknik Klasifikasi Data. Prosiding Seminar Nasional Hasil-Hasil Penelitian 2010 UNSRI.

Rohman, A. (2013). Penerapan Algoritma C4. 5 Berbasis Adaboost Untuk Prediksi Penyakit Jantung. Dinamika Sains, 11(26): 40–49.

Selya, A. S., & Anshutz, D. (2018). Machine Learning for the Classification of Obesity from Dietary and Physical Activity Patterns. In Advanced Data Analytics in Health (pp. 77–97). Springer.

Silwattananusarn, T., & Tuamsuk, K. (2012). Data mining and its applications for knowledge management: a literature review from 2007 to 2012. Nternational Jurnal of Data Mining and Knowledge Management Process, 2(5): 13–24.

Widayu, H., Nasution, S. D., Silalahi, N., & Mesran. (2017). Data Mining Untuk Memprediksi Jenis Transaksi Nasabah Pada Koperasi Simpan Pinjam Dengan Algoritma C4. 5. Media Informatika Budidarma, 1(2): 32–37.

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: practical machine learning tools and techniques (3rd ed). New York (US): Elsevier.

Yusa, M., & Sindu, W. (2017). Evaluasi Model Decision Tree C4. 5 Guna Prediksi Posibilitas Resiko Obesitas. Seminar Nasional Informatika (SNIf), 1(1), 147–152.

Downloads

Published

28-02-2020

How to Cite

Ente, D. R., Thamrin, S. A., Arifin, S., Kuswanto, H., & Andreza, A. (2020). KLASIFIKASI FAKTOR-FAKTOR PENYEBAB PENYAKIT DIABETES MELITUS DI RUMAH SAKIT UNHAS MENGGUNAKAN ALGORITMA C4.5. Indonesian Journal of Statistics and Its Applications, 4(1), 80–88. https://doi.org/10.29244/ijsa.v4i1.330

Issue

Section

Articles