Pemanfaatan metode SMOTE untuk prediksi periode donor darah dengan metode Backpropagation
Abstract
Prediction of blood donor periods is an important part of blood storage to ensure an adequate blood supply. The aim of this research is to compare the performance of the SMOTE method with an approach that does not use oversampling on the blood donor period dataset. The dataset used in this research includes information about blood donors, which consists of 4 attributes and 1 label. The experimental results show that the use of the SMOTE method significantly improves the predictability of blood donor periods. SMOTE successfully improves the accuracy, precision, recall, and F1 score of the classification model used. This indicates that the use of oversampling can address the class imbalance issue in the blood donor period dataset and improve the predictive ability of the model. This research makes an important contribution to the field of knowledge engineering by applying the SMOTE method to predict blood donor periods combined with Backpropagation. The results of this research can serve as a guide for the development of more efficient and effective blood donor prediction systems.
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References
W. Arditanti, A. Handojo, and T. Octavia. (2022). “Penerapan Artificial Neural Network dan Rule Based Classifier untuk Mengklasifikasikan Pendonor Darah Potensial pada Sistem Broadcast Pendonor,” J. Infra, vol. 10, no. 2, pp. 130–136.
Y. Astuti, Y. Astuti, P. Teknologi, B. Darah, J. Achmad, and Y. Yogyakarta. (2022) . “Shihatuna : Jurnal Pengabdian Kesehatan Masyarakat Pembentukan Peer group Sebagai Edukator Donor Darah Untuk Peningkatan Donor Sukarela pada Kelompok Remaja,” vol. 2, no. 2, pp. 51–56.
H. Wahono and D. Riana. (2020). “Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Naïve Bayes, K-Nearest Neighbors dan Decision Tree C4.5,” JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 1, p. 7. doi: 10.30865/jurikom.v7i1.1953.
P. R. Situmorang, W. Y. Sihotang, and L. Novitarum. (2020). “Identifikasi Faktor-Faktor yang Mempengaruhi Kelayakan Donor Darah di STIKes Santa Elisabeth Medan Tahun 2019,” J. Anal. Med. Biosains, vol. 7, no. 2, p. 122, doi: 10.32807/jambs.v7i2.195.
M. Mustaqim, B. Warsito, and B. Surarso. (2019). “Kombinasi Synthetic Minority Oversampling Technique (SMOTE) dan Neural Network Backpropagation untuk menangani data tidak seimbang pada prediksi pemakaian alat kontrasepsi implan,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 5, no. 2, p. 128, 2019, doi: 10.26594/register.v5i2.1705.
P. Galetsi, K. Katsaliaki, and S. Kumar. (2019). “Big data analytics in health sector: Theoretical framework, techniques and prospects,” Int. J. Inf. Manage., vol. 50, no. May, pp. 206–216, 2020, doi: 10.1016/j.ijinfomgt.2019.05.003.
R. Studer, V. R. Benjamins, and D. Fensel. (1998). “Knowledge Engineering: Principles and methods,” Data Knowl. Eng., vol. 25, no. 1–2, pp. 161–197, , doi: 10.1016/S0169-023X(97)00056-6.
D. Husen, D. Sandi, and S. Bumbungan. (2022). “Analisis Prediksi Kebakaran Hutan dengan Menggunakan Algoritma Random Forest Classifier Kebakaran hutan dan lahan di Indonesia telah menjadi perhatian dunia internasional khususnya sejak kebakaran hutan yang terjadi pada tahun 80-an [ 2 ]. Penyebab kebaka,” vol. 16, pp. 150–155.
L. N. Rupaidah, M. H. Syarif, and U. Enri. (2021). “Perbandingan Algoritma Sarima Dan Linear Regression Dalam Memprediksi Indeks Harga Saham Gabungan,” Krea-TIF J. Tek. Inform., vol. 9, no. 2, pp. 42–49, , doi: 10.32832/kreatif.v9i2.6291.
“View of PKM Donor Darah.pdf.”
S. Sofyan and A. Prasetyo. (2021). “Penerapan Synthetic Minority Oversampling Technique (SMOTE) Terhadap Data Tidak Seimbang Pada Tingkat Pendapatan Pekerja Informal Di Provinsi D.I. Yogyakarta Tahun 2019,” Semin. Nas. Off. Stat., vol. 2021, no. 1, pp. 868–877, 2021, doi: 10.34123/semnasoffstat.v2021i1.1081.
E. Sutoyo and M. A. Fadlurrahman. (2020). “Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Television Advertisement Performance Rating Menggunakan Artificial Neural Network,” J. Edukasi dan Penelit. Inform., vol. 6, no. 3, p. 379, 2020, doi: 10.26418/jp.v6i3.42896.
R. Siringoringo. (2018). “Klasifikasi data tidak Seimbang menggunakan algoritma SMOTE dan k-nearest neighbor,” J. ISD, vol. 3, no. 1, pp. 44–49, 2018.
A. Santoso and S. Hansun. (2019). “Prediksi IHSG dengan Backpropagation Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 313–318, 2019, doi: 10.29207/resti.v3i2.887.
H. Amarullah Purwaatmaja Ash-Shidiq EFSA and K. Muslim Lhaksmana. (2021). “Prediksi Retweet Menggunakan Fitur Berbasis Pengguna dan Fitur Berbasis Konten dengan Metode Klasifikasi ANN,” vol. 8, no. 5, pp. 11207–11215.
G. Zaida Muflih. (2021). “Penentuan Parameter Pembelajaran Jaringan Saraf Tiruan Backpropagation dan Pengaruhnya Terhadap Proses Pelatihan,” vol. 1, no. 2, pp. 12–17, doi: 10.53863/juristik.v1i02.363.
P. R. Sihombing. (2020). “Perbandingan Metode Artificial Neural Network (ANN) dan Support Vector Machine (SVM) untuk Klasifikasi Kinerja Perusahaan Daerah Air Minum (PDAM) di Indonesia,” J. Ilmu Komput., vol. 13, no. 1, p. 9, 2020, doi: 10.24843/jik.2020.v13.i01.p02.
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