Penerapan Algoritma Decision Tree C4.5 Untuk Memprediksi Kelayakan Calon Pendonor Melakukan Donor Darah Dengan Klasifikasi Data Mining
DOI:
https://doi.org/10.35746/jtim.v2i4.75Kata Kunci:
Data Mining, C4.5 Algorithm, Web Applications, Blood DonorsAbstrak
Based on data from UDD PMI Kampar Regency, many donors must have provisions to become blood donors. So far, blood donor selection has been made manually to determine whether potential donors can donate blood or not. Meanwhile, today's information system has not yet explored further information from the large amount of data stored as knowledge. There is a need for organizational consolidation and continuous evaluation of the performance that has been carried out by PMI in dealing with social and humanitarian problems. By making a data mining application with a classification method using the Decision Tree C4.5 Algorithm in predicting someone worthy or not to donate blood, it can be calculated from the results of variables that are continuous or critical, such as variables of age, body weight, hemoglobin (HB) levels, blood pressure. (systolic and diastolic), The data that enters the information system is calculated using the Decision Tree C4.5 Algorithm formula, which results in detailed results and can produce valid and more accurate values. With the data mining application using the Decision Tree Algorithm C4.5 method, potential blood donors' eligibility can be classified based on age, body weight, hemoglobin, and blood pressure. Hemoglobin with the highest gain value (0.861212618) is the variable that most determines blood donation success.
Unduhan
Referensi
B. Andriska Candra P, “Prediksi Kepuasan Mahasiswa Terhadap Tingkat Pelayanan Menggunakan Algoritma C4.5 (Decision Tree ) (Studi Kasus : Fakultas Teknik Universitas Hamzanwadi ),” Infotek J. Inform. dan Teknol., vol. 1, no. 1, pp. 32–39, 2018.
R. Ordila, R. Wahyuni, Y. Irawan, and M. Y. Sari, “Penerapan Data Mining Untuk Pengelompokan Data Rekam Medis Pasien Berdasarkan Jenis Penyakit Dengan Algoritma Clustering …,” J. Ilmu Komput., vol. 9, no. 2, pp. 148–153, 2020, [Online]. Available: https://jik.htp.ac.id/index.php/jik/article/view/181.
Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” J. Edik Inform., vol. 2, no. 2, pp. 213–219, 2017.
P. Meilina, “Penerapan Data Mining dengan Metode Klasifikasi Menggunakan Decision Tree dan Regresi,” J. Teknol. Univ. Muhammadiyah Jakarta, vol. 7, no. 1, pp. 11–20, 2015, [Online]. Available: jurnal.ftumj.ac.id/index.php/jurtek.
Yuda Irawan, "Implementation Of Data Mining For Determining Majors Using K-Means Algorithm In Students Of SMA Negeri 1 Pangkalan Kerinci," J. Appl. Eng. Technol. Sci., vol. 1, no. 1, pp. 17–29, 2019, doi: 10.37385/jaets.v1i1.18.
Y. Yulisman, R. Wahyuni, and Y. Irawan, “Aplikasi Pengarsipan Surat Masuk dan Surat Keluar Berbasis Web pada SMP Negeri 32 Pekanbaru,” J. Teknol. Sist. Inf. dan Apl., vol. 3, no. 4, p. 252, 2020, doi: 10.32493/jtsi.v3i4.7345.
B. Mustika, “Implementasi Text Mining Pada Twitter Dengan Algoritma K-Means Clustering Sebagai Dasar Kebijakan Marketing Biro Perjalanan Wisata,” J. Ilmu Komput., vol. 9, no. 2, pp. 138–147, 2020, doi: doi.org/10.33060/JIK/2020/Vol9.Iss2.180.
Y. Irawan, “Penerapan data mining untuk evaluasi data penjualan menggunakan metode clustering dan algoritma hirarki divisive,” Jtiulm, vol. 3, no. 1, pp. 13–20, 2019.
