Penerapan Decision Tree Menggunakan Algoritma C4.5 Untuk Deteksi Demam Berdarah Pada RS. IMC Bintaro

Farid Fadli, Belsana Butar Butar

Abstract


Abstract: According to the WHO report in 2004, Indonesia is the largest country with the highest number of sufferers and death rates due to dengue fever. If it is not handled properly, the postponed treatment can be fatal. In this study, the authors used the kepuutsan tree method with C4.5 algorithm to process patient data to predict whether patients experienced bloody help regarding existing indications with the help of Rapidminer software. The results of data processing using Rapidminer were evaluated and validated with a confussion matrix and AUC curve, the results of data processing using the C4.5 algorithm had an accuracy of 72% and AUC had a value of 0.758 with a fair classification category.

Keywords: Algorithm C4.5, Decision Tree, Data Mining

Full Text:

PDF

References


Amalia, H., & Evicienna. (2013). Sistem Penunjang Keputusan Kesehatan Untuk Hipertensi Menggunakan Algoritma C4.5. Jurnal Pilar Nusa Mandiri, IX(1, Maret), 15–22.

Arsin, A. A. (2013). Epidemiologi Demam Berdarah Denguw (DBD) Di Indonesia. (A. Sade, Ed.). Masagena Press.

Budihartanti, C. (2013). Penerapan Data Mining Berdasarkan Asosiasi Menggunakan Algoritma Apriori Dalam Penentuan Pola Belanja Kitchen Apliances. Techno Nusa Mandiri, IX(1 Maret), 20–28.

Han, J. (2001). Data Mining: Concept and Techniques. Morgan Kaufmann.

Hermawati, F. A. (2013). Data Mining. (Putri Christian, Ed.) (Ed.I). Yogyakarta: CV. Andi Offset.

Heryanti, D. N. (2005). Data Mining Classification Dengan Metode Decision Tree Menggunakan Algoritma C4.5 Data Mining Classification With Decision Tree Method Using C4.5 Algorithma. In Tugas Akhir. Bandung: Telkom University. Retrieved from https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwi-vanz6ojjAhWJr48KHYKfA9kQFjABegQIBRAC&url=https%3A%2F%2 Fopenlibrary.telkomuniversity.ac.id%2Fpustaka%2Ffiles%2F94458%2Fresume%2Fdata-mining-classification-dengan-metode-decision-tree-menggunakan-algoritma-c4-5-data-mining-classification-with-decision-tree-method-using-c4-5-algorithm.pdf&usg=AOvVaw3dCpQ5sZpXEPljZuA1ts2v

Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. Yogyakarta: Andi.

Purnia, D. S., & Warnilah, A. I. (2017). Implementasi Data Mining Pada Penjualan Kacamata Menggunakan Algoritma Apriori. IJCIT (Indonesia Journal on Computer and Information Technology), 2(2, Nopember,ISSN: 2527-449X E-ISSN: 2549-7421), 31–39.

Rusito, & Firmansyah, M. T. (2016). Implementasi Metode Decision Tree dan Algoritma C4.5 Untuk Klasifikasi Data Nasabah Bank. Jurnal Infokom, (1 Maret).

Siregar, A. M., & Puspabhuana, A. (2002). NDATA MINING: Pengolahan Data Menjadi Informasi dengan RapidMiner. Surakarta: Kekata.

Sulianta, F., & Juju, D. (2010). Data Mining (Meramalkan Bisnis Perusahaan). Jakarta: Gramedia.

Suyanto. (2007). Artificial Intelligence : Searching, Reasoning, Planning dan Learning. Bandung: Informatika.




DOI: https://doi.org/10.31294/ijse.v5i1.5866



 

ISSN : 2714-9935 


Published by LPPM Universitas Bina Sarana Informatika

Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Kota Jakarta Pusat, DKI Jakarta 10450


This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License