PENERAPAN ALGORITMA C4.5 UNTUK MEMBUAT MODEL PREDIKSI PASIEN YANG MENGIDAP PENYAKIT DIABETES

  • Nanto Sunanto universitas muhammadiyah riau (UMRI)
  • Ghazi Falah Universitas Muhammadiyah Riau

Abstract

Diabetes is a disease in which the pancreas cannot produce insulin properly. Insulin is a hormone produced from the pancreas, which is useful as a door to channel glucose from food that is absorbed to be flowed into blood cells so that the body can produce energy. Meanwhile, according to WHO diabetes is a very deadly disease which is ranked 9th in the world. People with diabetes generally die from damage to several vital organs such as the heart, kidneys and liver. The early sufferers of diabetes are not known with certainty so that diabetics who have been treated are in a worrying condition. To reduce the number of deaths due to diabetes, a system is needed that can identify early symptoms of diabetes, so that people with diabetes can be handled properly. Data mining technology can help build a system to predict diabetes using the C4.5 decision tree algorithm. In this study, diabetes prediction data was taken from the UCI Repository. Then the data is processed in stages, select data, pre-processing and split validation using rapid miner. The results of data processing using a rapid miner, in the form of rules that can be used to predict diabetes. The rules generated from the RapidMiner decision tree have an accuracy of 95.51%.

Keywords: sufferers, diabetes, insulin, data mining, RapidMiner

References

S. Pangribowo, “Infodatin-2020-Diabetes-Melitus.pdf.” 2020.

M. B. Hanif and Khoirudin, “Sistem Aplikasi Prediksi Penyakit diabetes Menggunakan Fiture Selection Korelasi Pearson dan Klasifikasi Naïve Bayes,” Pengemb. Rekayasa dan Teknol., vol. 16, no. 2, pp. 199–205, 2020.

A. Ridwan, “Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Mellitus,” J. SISKOM-KB (Sistem Komput. dan Kecerdasan Buatan), vol. 4, no. 1, pp. 15–21, 2020, doi: 10.47970/siskom-kb.v4i1.169.

Putri, E. Ucha, Sanni Irawan, and F. Rizky, “Implementasi Data Mining Untuk Prediksi Penyakit Diabetes,” KESATRIA( J. penerapan Sist. Inf. dan Manaj., vol. 2, no. 1, pp. 39–46, 2021.

Y. A. Fiandra, S. Defit, and Y. Yuhandri, “Penerapan Algoritma C4.5 untuk Klasifikasi Data Rekam Medis berdasarkan International Classification Diseases (ICD-10),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 2, pp. 82–89, 2017, doi: 10.29207/resti.v1i2.48.

I. Lishania, R. Goejantoro, and Y. N. Nasution, “Perbandingan Klasifikasi Metode Naive Bayes dan Metode Decision TreeAlgoritma (J48) pada Pasien Penderita Penyakit Stroke di RSUD Abdul Wahab Sjahranie Samarinda,” J. Eksponensial, vol. 10, no. 2, pp. 135–142, 2019.

A. Mujahidin and D. Pribadi, “Penerapan algoritma C4 . 5 untuk diagnosa penyakit pneumonia pada anak balita berbasis mobile,” J. Swabumi, vol. 5, no. 2, pp. 155–161, 2017, [Online]. Available: https://ejournal.bsi.ac.id/ejurnal/index.php/swabumi/article/view/2523.

E. P. K. Orpa, E. F. Ripanti, and T. Tursina, “Model Prediksi Awal Masa Studi Mahasiswa Menggunakan Algoritma Decision TreeC4.5,” J. Sist. dan Teknol. Inf., vol. 7, no. 4, p. 272, 2019, doi: 10.26418/justin.v7i4.33163.

S. A. Alasadi and W. S. Bhaya, “Review of data preprocessing techniques in data mining,” J. Eng. Appl. Sci., vol. 12, no. 16, pp. 4102–4107, 2017, doi: 10.3923/jeasci.2017.4102.4107.

J. P. Fortin, T. J. Triche, and K. D. Hansen, “Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi,” Bioinformatics, vol. 33, no. 4, pp. 558–560, 2017, doi: 10.1093/bioinformatics/btw691.

A. Massaro, V. Maritati, and A. Galiano, “Data Mining Model Performance of Sales Predictive Algorithms Based on Rapidminer Workflows,” Int. J. Comput. Sci. Inf. Technol., vol. 10, no. 3, pp. 39–56, 2018, doi: 10.5121/ijcsit.2018.10303.

G. Mahajan, B. Saini, and T. Almas, “Taxonomy on RapidMiner Using Machine Learning,” SSRN Electron. J., pp. 2410–2415, 2019, doi: 10.2139/ssrn.3363071.

S. R. Phandinata, E. T. Atmodiwirjo, and D. Basaria, “Developmental Individual-Differences Relationship-Based (Dir) Floortime Dalam Meningkatkan Komunikasi Dua Arah Pada Kasus Autism Spectrum Disorder (Asd),” Psibernetika, vol. 10, no. 2, pp. 103–113, 2017, doi: 10.30813/psibernetika.v10i2.1046.

U. Celik and C. Basarir, “The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner,” Alphanumeric J., vol. 5, no. 1, pp. 45–45, 2017, doi: 10.17093/alphanumeric.290381.

A. Purwanto, M. Asbari, and T. I. Santoso, “Analisis Data Penelitian Marketing: Perbandingan Hasil antara Amos, SmartPLS, WarpPLS, dan SPSS untuk Jumlah Sampel Besar,” J. Ind. Eng. Manag. Res., vol. 2, no. 4, pp. 216–227, 2021, [Online]. Available: https://www.jiemar.org/index.php/jiemar/article/view/178/138.

Published
2022-07-10
How to Cite
[1]
N. Sunanto and G. Falah, “PENERAPAN ALGORITMA C4.5 UNTUK MEMBUAT MODEL PREDIKSI PASIEN YANG MENGIDAP PENYAKIT DIABETES”, rabit, vol. 7, no. 2, pp. 208-216, Jul. 2022.
Section
Articles
PDF (Bahasa Indonesia)
Abstract views: 260
downloads: 162