CLASSIFICATION OF BLOOD DONOR DATA USING C4.5 AND K-NEAREST NEIGHBOR METHODS (CASE STUDY: UTD PMI BALI PROVINCE)

  • Ni Ketut Melly Astuti (1*) STMIK Primakara
  • Nengah Widya Utami (2) STMIK Primakara
  • I Gede Putu Krisna Juliharta (3) STMIK Primakara

  • (*) Corresponding Author
Keywords: data mining, classification, blood donation, C4.5, K-Nearest Neighbor

Abstract

Classification of blood donor data at UTD PMI Bali Province by applying the C4.5 and K-Nearest Neighbor algorithms. The number of blood donor data donors is 34,948, of which 90% of the data, namely 31,454 is used as training data. Meanwhile, 10% of the data, which is 3,494 data, is used as the implementation of data testing using the Orange application. C4.5 obtained an accuracy score of 92.9%, F1 of 92.2%, Precision of 93.1%, Recall of 92.9%, specificity of 68.2%. While K-nearest neighbor obtained an accuracy score of 91%, F1 of 90.1%, Precision of 90.8%, Recall of 91%, specificity of 63%. With the AUC (Area Under Curve) value for the C4.5 algorithm is 0.875 and the K-nearest neighbor is 0.813 Good Classification. The results of the evaluation using the confusion matrix C4.5 obtained an accuracy score of 92.6%, F1 of 95.7%, Precision of 99.4%, Recall of 92.4%, specificity of 96%. While k-nearest neighbor obtained an accuracy score of 90.9%, F1 of 94.6%, Precision of 98.4%, Recall of 91.2%, specificity of 88.4%. Based on the evaluation of the confusion matrix and the ROC Analysis Graph, the C4.5 algorithm obtained higher results than the K-Nearest Neighbor algorithm. Based on the data on the characteristics of blood donors at UTD PMI Bali Province, it shows that the gender is male, Badung area, Age 20 to < 30, the occupation of private employees dominates in blood donation.

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References

Amalia, H. (2018). Perbandingan Metode Data Mining SVM Dan NN Untuk Klasifikasi Penyakit Ginjal Kronis. Maret, 14(1), 1. Retrieved from www.bsi.ac.id

Djuardi, A. M. P. (2020). Donor Darah Saat Pandemi COVID-19. Jurnal Medika Hutama, 02(01), 298–303.

Firdaus, M. R., Latif, A., & Gata, W. (2020). Klasifikasi Kelayakan Calon Pendonor Darah Menggunakan Neural Network. Sistemasi, 9(2), 362. https://doi.org/10.32520/stmsi.v9i2.840

Florin Gorunescu. (2011). Data Mining Concepts, Models and Techniques. Verlag Berlin Heidelrbeg: Springer.

Haudi, S.Pd., M.M., D. B. . (2021). Teknik Pengambilan Keputusan (C. Hadion Wijoyo, S.E., S.H., S.Sos.M.H., M.M., AK., Ed.). Sumatra Barat: CV Insan Cendekia Mandiri. Retrieved from https://books.google.co.id/books?hl=id&lr=&id=hPgkEAAAQBAJ&oi=fnd&pg=PA111&dq=ID3+memiliki+keunggulan+yang+dapat+mengubah+data+yang+memerlukan+pengambilan+keputusan+yang+sebelumnya+bersifat+kompleks+dan+global+menjadi+simple+dan+spesifik.&ots=7UBpfaNBI8&s

Indonesia, M. K. R. (2015). Peraturan Menteri Kesehatan RI No 91 Tahun 2015 Tentang Standar Pelayanan Transfusi Darah. 2009, 1–27. Retrieved from http://hukor.kemkes.go.id/uploads/produk_hukum/PMK_No._91_ttg_Standar_Transfusi_Pelayanan_Darah_.pdf

Jejaring. (2019). Tahap-Tahapan Knowladge Discovery In Database (KDD). Retrieved September 11, 2021, from https://www.jejaring.web.id/tahap-tahapan-knowladge-discovery-in-database-kdd/

Kodati, S., & Vivekanandam, R. (2018). Analysis of Heart Disease using in Data Mining Tools Orange and Weka Sri Satya Sai University Analysis of Heart Disease using in Data Mining Tools Orange and Weka. Global Journal of Computer Science and Technology, 18(1).

Nengah Widya Utami, A. A. I. I. P. (2021). Penerapan Data Mining Untuk Mengetahui Pola Pemilihan Program Studi Di Stmik Primakara Menggunakan Algoritma K-Means …. Jurnal Teknologi Informasi Dan …, 3, 456–463. Retrieved from http://jurnal.undhirabali.ac.id/index.php/jutik/article/view/1540

Orangedatamining.com. (2021). “ROC Analysis.” Retrieved September 20, 2021, from https://orangedatamining.com/widget-catalog/evaluate/rocanalysis/

Pahlevi, R., Fredlina, K. Q., & Utami, N. W. (2021). Penerapan Algoritma Id3 Dan Svm Pada Klasifikasi Penyakit Diabetes Melitus Tipe 2. Prosiding Snast, 2, 64–75. Retrieved from https://ejournal.akprind.ac.id/index.php/prosidingsnast/article/view/3340

Purnia, A. L. ; D. S. (2019). Implementasi Data Mining Untuk Mengetahui Faktor Kelayakan Donor Darah UTD Kota Tasikmalaya Menggunakan Algoritma C4.5. 14(1), 145–150.

Suyanto. (2017). Data Mining:Untuk Klasifikasi Dan Klasterisasi Data. Bandung: Informatika.

Utami, N. W., Sukajaya, I. N., Made Candiasa, I., & Dewi, E. G. A. (2019). The implementation of data mining to show UKT (students’ tuition) using fuzzy C-means algorithm: (Case study: Universitas Pendidikan Ganesha). 2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019, 101–106. https://doi.org/10.1109/ICACSIS47736.2019.8979933

Wahono, H., & Riana, D. (2020). Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Naïve Bayes, K-Nearest Neighbors dan Decision Tree C4.5. JURIKOM (Jurnal Riset Komputer), 7(1), 7. https://doi.org/10.30865/jurikom.v7i1.1953

Published
2022-03-09
How to Cite
Astuti, N. K., Utami, N., & Juliharta, I. G. P. (2022). CLASSIFICATION OF BLOOD DONOR DATA USING C4.5 AND K-NEAREST NEIGHBOR METHODS (CASE STUDY: UTD PMI BALI PROVINCE). Jurnal Pilar Nusa Mandiri, 18(1), 9-16. https://doi.org/10.33480/pilar.v18i1.2790
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