Penerapan Algoritma C4.5 dalam Mengidentifikasi Karakteristik Pasien Beresiko Diabetes
Keywords:
C4.5 algorithm, diabetes, decision treeAbstract
Diabetes Mellitus is a disease characterized by an increase in glucose as well as an abnormal rise in blood sugar concentration due to insulin deficiency. The International Diabetes Federation (IDF) reports that in 2021, approximately 540 million people worldwide were affected by diabetes, and this number is expected to increase further if the general public's lack of awareness about symptoms that can trigger the diabetes disease continues. This research aims to implement the C4.5 algorithm in predicting diabetes mellitus based on acquired data. The amount of data used is 300 records, where 90% of the data serves as training data and the remaining 10% is test data. The data consists of 6 attributes: age, gender, hypertension, glucose, heart disease, and BMI (Body Mass Index). Based on Gain calculations, the Glucose attribute becomes the root of the decision tree. The tested data in this study achieved an accuracy rate of 77%, precision of 82%, and recall of 64%.
References
A. Najib, D. Nurcahyono, And R. P. P. Setiawan, “Klasifikasi Diagnosa Penyakit Diabetes Mellitus (Dm) Menggunakan Algoritma C4.4,” Just Ti J. Sains Terap. Teknol. Inf., Vol. 11, No. 2, P. 47, Jul. 2019, Doi: 10.46964/Justti.V11i2.153.
M. Sukmadani Rusdi, “Hipoglikemia Pada Pasien Diabetes Melitus,” J. Syifa Sci. Clin. Res., Vol. 2, No. 2, Pp. 83–90, Aug. 2020, Doi: 10.37311/Jsscr.V2i2.4575.
O. Ozougwu, “The Pathogenesis And Pathophysiology Of Type 1 And Type 2 Diabetes Mellitus,” J. Physiol. Pathophysiol., Vol. 4, No. 4, Pp. 46–57, Sep. 2013, Doi: 10.5897/Jpap2013.0001.
I. Menarianti, “Klasifikasi Data Mining Dalam Menentukan Pemberian Kredit Bagi Nasabah Koperasi,” J. Ilm. Teknosains, Vol. 1, No. 1, 2015.
A. A. Robbani, A. M. Siregar, And D. S. Kusumaningrum, “Klasifikasi Penderita Penyakit Diabetes Menggunakan Algoritma C4.5,” No. 1, 2022.
I. Sutoyo, “Implementasi Algoritma Decision Tree Untuk Klasifikasi Data Peserta Didik,” J. Pilar Nusa Mandiri, Vol. 14, No. 2, P. 217, Sep. 2018, Doi: 10.33480/Pilar.V14i2.926.
Y. Mardi, “Data Mining: Klasifikasi Menggunakan Algoritma C4.5,” Edik Inform., Vol. 2, No. 2, Pp. 213–219, Feb. 2017, Doi: 10.22202/Ei.2016 V2i2.1465.
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. Inf., Vol. 1, No. 2, Pp. 82–89, Oct. 2017, Doi: 10.29207/Resti.V1i2.48.
B. Sugara, D. Widyatmoko, B. S. Prakoso, And D. M. Saputro, “Penerapan Algoritma C4.5 Untuk Deteksi Dini Gangguan Autisme Pada Anak,” 2018.
E. M. Sipayung, H. Maharani, And I. Zefanya, “Perancangan Sistem Analisis Sentimen Komentar Pelanggan Menggunakan Metode Naive Bayes Classifier,” J. Sist. Inf., Vol. 8, No. 2, 2016.
M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, And R. Budiarto, “Evaluating Trust Prediction And Confusion Matrix Measures For Web Services Ranking,” Ieee Access, Vol. 8, Pp. 90847–90861, 2020, Doi: 10.1109/Access.2020.2994222.[12] E. W. H. Candana, I. G. A. Gunadi, And D. G. H. Divayana, “Jurnal Ilmu Komputer Indonesia (Jik) , Volume 6 , No : 2, November 2021 P-Issn : 2615-2703 (Print) Dan E-Issn: 2615-2711 (Online),” Vol. 6, 2021.
R. Novendri, R. Andreswari, And O. N. Pratiwi, “Implementasi Data Mining Untuk Memprediksi Customer Churn Menggunakan Algoritma Naive Bayes”.
T. T. Hanifa And S. Al-Faraby, “Analisis Churn Prediction Pada Data Pelanggan Pt. Telekomunikasi Dengan Logistic Regression Dan Underbagging”.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK)
This work is licensed under a Creative Commons Attribution 4.0 International License.
http://creativecommons.org/licenses/by/4.0
