Prediksi Potensi Relawan Pendonor Darah Menjadi Pendonor Darah Tetap dengan Penerapan Metode Klasifikasi Decision Tree
Main Article Content
Abstract
Blood donation is an important activity to obtain blood as a raw material into the blood supply chain. If there is not enough blood in the human body, then human survival will be threatened, for some conditions blood transfusions are required, such as accidents, childbirth or certain grades of dengue fever. UTD PMI Pekanbaru City is the organizing body for blood donation activities in the process of helping and serving the blood needs for public. Based on data from the Ministry of Health in 2019, Pekanbaru City lacked in blood stock 32.4 percent, which the ideal supply of blood bags in Pekanbaru City was 130,019 blood stock. This causes some hospitals difficult to find the supply of blood stock. The cause of lacking in blood bags' availability in Pekanbaru City was the number of volunteer donors fluctuates and the public's low interest in becoming volunteer blood donors. So it becomes a problem when the number of requests for blood increases, while the supply at the blood bank is running low. The method used in this research was the Decision Tree method. The algorithm used in this study was the C.45 Algorithm. To solve the problems that occur, data analysis of blood donor volunteers was carried out. Based on the results of the testing data analysis as many as 50 records, 6 rules were produced which can be concluded that age over 19 years with an entrepreneur job has the potential to become a permanent blood donor
Article Details
References
[2]World Health Organization (WHO). (2010). Towards 100% Voluntary Blood Donation, A Global Framework for action Geneva, WHO Press.
[3]Kementrian Kesehatan RI. (2019). Situasi Donor Darah di Indonesia. InfoDatin: Pusat Data dan Informasi Kementrian Kesehatan RI.
[4]Wahono, Hermanto & Dwiza Riana. (2020). Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Naive Bayes, K-Nearest Neighbors dan Decision Tree C.45. Jurnal Riset Komputer, 7(1), 7-14. https://doi.org/10.30865/jurikom.v7i1.1953
[5]Muslim, Much Aziz., Aldi Nurzahputra., & Budi Prasetiyo. (2018). Improving Accuracy of C.45 Algorithm Using Split Feature Reduction Model and Bagging Ensemble for Credit Card Risk Prediction. International Conference on Information and Communications Technology (ICOIACT), 141-145. https://doi.org/10.1109/ICOIACT.2018.8350753
[6]Adhatrao,Kalpesh., Aditya Gaykar., Amiraj Dhawan., Rohit Jha ., & Vipul Honrao. (2013). Predicting Student’s Performance Using ID3 and C.45 Classification Algorithm’s. International Journal of Data Mining & Knowledge Management Process. (IJDKP), 3(5), 39-52. https://doi.org/10.5121/ijdkp.2013.3504
[7]Badi’auzzaman, Iffah Syafiqah Meor., Moey Soo Foon,., Mohd. Zulfaezal Che Azemin., Mohd. Izzuddin., Mohd. Tamrin. (2019). The Use of Decision Tree in Breast Cancer-Related Research: a Scoping Analysis Based on Scopus-Indexed Articles. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(9), 1344-1355. https://doi.org/10.35940/ijitee.I3290.0789S319
[8]Wiza, Fana & Bayu Febriadi. (2019). Classification Analysis Using C 4.5 Algorithm to Predict the Level of Graduation of Nurul Falah Pekanbaru High School Student. International Journal of Information System & Technology, 2(2), 43-52. https://doi.org/10.30645/ijistech.v2i2.21
[9]Supriyadi, Didi & S. Thya Safitri. (2020). The Application of C.45 Algorithm to Classify the User Satisfaction of Online Learning System. Journal of Information System & Technology, 3(2), 323-331. https://doi.org/10.30645/ijistech.v3i2.67
[10] Pradipta, Asro., Dedy Hartama., Anjar Wanto., Saifullah., & Jalaluddin. (2019). The Application of Data Mining in Determining Timely Graduation Using the C 45 Algorithm. Journal of Information System & Technology, 3(1), 31-36. https://doi.org/10.30645/ijistech.v3i1.30
[11] Nguyen, Thang Van., Van Dung Nguyen., Thi-Thu Nguyen., Phung Cong Phi Khanh., Tien-Anh Nguyen., Duc-Tan Tran. (2020). Motorsafe: An Android Application for Motorcyclists Using Decision Tree Algorithm. International Journal of Interactive Mobile Technologies, 14(2), 119-129. https://doi.org/10.3991/ijim.v14i02.10742
[12] Abana, Ertie C. (2019). A Decision Tree Approach for Predicting Student Grades in Research Project using WEKA. International Journal of Advance COmputer Science and Application, 10(7), 285-289. https://doi.org/10.14569/IJACSA.2019.0100739
[13] Chynthia, Eka Pandu & Edi Ismanto. (2019). Metode Decision Tree Algoritma C.45 Dalam Mengklasifikasi Data Penjualan Bisnis Gerai Makanan Cepat Saji. Jurnal Riset Sistem Infromasi dan Teknik Informatika, 3(1), 1-13. https://doi.org/10.30645/jurasik.v3i0.60
[14]Izyuddin, Ade & Setyawan Wibisono. (2020). Aplikasi Prediksi Penjualan AC Menggunakan Decision Tree Dengan Algoritma C.45. Jurnal Manajemen Informatika & Sistem Informasi, 3(2), 146-156. https://doi.org/10.36595/misi.v3i2.208
[15] Dengen, Christin Nandari., Kusrini., & Emha Taufiq Luthfi. (2020). Implementasi Decision Tree untuk Prediksi Kelulusan Mahasiswa Tepat Waktu. Jurnal Ilmiah SISFOTENIKA, 10(1), 1-11. https://doi.org/10.30700/jst.v10i1.484
[16]Sembiring, Muhammad Ardiansyah., Mustika Fitri Larasati Sibuea., & Andy Sapta. (2018). Analisa Kinerja Algoritma C.45 Dalam Memprediksi Hasil Belajar. Journal of Science and Social Research, 1(1), 73-79. https://doi.org/10.33330/jssr.v1i1.110
[17]Elfaladonna, Febie & Ayu Rahmadani. (2019). Analisa Metode Classication-Decision Tree dan Algortima C.45 Untuk Memprediksi Penyakit Diabetes Dengan Menggunakan Aplikasi RapidMiner. Science and Information Technology Journal, 2(1), 10-17. https://doi.org/10.31598/sintechjournal.v2il.293