Artificial Neural Network-Based Prediction Model Back Propagation on Blood Demand and Blood Supply

Frengky  Tedy; Patrisius  Batarius; Ign. Pricher A. N.  Samane; Alfry Aristo Jansen  Sinlae

doi:10.29207/resti.v7i6.5508

Frengky Tedy Universitas Katolik Widya Mandira
Patrisius Batarius Universitas Katolik Widya Mandira
Ign. Pricher A. N. Samane Universitas Katolik Widya Mandira
Alfry Aristo Jansen Sinlae Universitas Katolik Widya Mandira http://orcid.org/0000-0002-8228-3555

DOI: https://doi.org/10.29207/resti.v7i6.5508

Keywords: Neural Network, Backpropagation, Blood demand, Blood supply

Abstract

The balance between blood demand and supply at the Indonesian Red Cross Blood Transfusion Unit (UTD-PMI) is crucial. This condition must be maintained to reduce unused or expired blood supplies. Despite the situation in UTD-PMI, where the blood supply exceeds demand, there is still a shortage of blood when needed by patients. This research aims to model the prediction of blood demand and supply for each blood type using the Back Propagation artificial neural network approach. Data from the last 3 years, from 2020 to 2022, were utilized in this research process. There are three stages in this research process. The first stage involves the training process, using data from January 2020 to December 2021. The testing process utilizes data from January 2021 to December 2022. The prediction process involves displaying the forecasted data for the next 12 months from January to December 2023. The accuracy of the calculations is assessed using the mean square error (MSE). Ultimately, the research results present the prediction model for the four types of blood with respect to the demand and supply. These findings can serve as a reference to regulate future blood donation activities carried out by the UTD-PMI.

Downloads

Download data is not yet available.

References

C. Mouncif and A. Bellabdaoui, “Blood collection supply chain management: A critical review and future perspective,” in 6th International Conference on Optimization and Applications, ICOA 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., Apr. 2020. doi: 10.1109/ICOA49421.2020.9094514.

H. Himawan and P. D. Silitonga, “Journal of Critical Reviews COMPARISON OF FORECASTING ACCURACY RATE OF EXPONENTIAL SMOOTHING METHOD ON ADMISSION OF NEW STUDENTS,” 2019, doi: 10.31838/jcr.07.02.50.

S. Ghasemi, “The Location Allocation Problem of After Disaster Blood Supply Chain,” in 2019 15th Iran International Industrial Engineering Conference (IIIEC), 2019, pp. 127–131. doi: 10.1109/IIIEC.2019.8720635.

F. Lestari, U. Ulfah, F. R. Aprianis, and S. Suherman, “Inventory Management Information System in Blood Transfusion Unit,” in IEEE International Conference on Industrial Engineering and Engineering Management, IEEE Computer Society, Jan. 2019, pp. 268–272. doi: 10.1109/IEEM.2018.8607557.

M. Naghipour and M. Bashiri, “Designing a Bi-Objective Stochastic Blood Supply Chain Network in a Disaster,” in Proceedings of 2019 15th Iran International Industrial Engineering Conference, IIIEC 2019, Institute of Electrical and Electronics Engineers Inc., May 2019, pp. 171–177. doi: 10.1109/IIIEC.2019.8720727.

P. A. J. Sandaruwan, U. D. L. Dolapihilla, D. W. N. R. Karunathilaka, W. A. D. T. L. Wijayaweera, W. H. Rankothge, and N. D. U. Gamage, “Towards an Efficient and Secure Blood Bank Management System,” in IEEE Region 10 Humanitarian Technology Conference, R10-HTC, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/R10-HTC49770.2020.9356980.

S. Abdelall, D. Baroud, S. Alalamy, I. Alrass, and S. Agha, “The use of discrete event simulation for optimal performance of blood banks (A Case Study of Al-Shifa Central Blood Bank),” in Proceedings - 2020 International Conference on Assistive and Rehabilitation Technologies, iCareTech 2020, Institute of Electrical and Electronics Engineers Inc., Aug. 2020, pp. 36–40. doi: 10.1109/ICARETECH49914.2020.00014.

Meizir and B. Rikumahu, “Prediction of Agriculture and Mining Stock Value Listed in Kompas100 Index Using Artificial Neural Network Backpropagation,” in 2019 7th International Conference on Information and Communication Technology (ICoICT), 2019, pp. 1–5. doi: 10.1109/ICoICT.2019.8835284.

Y. Weng, X. Wang, J. Hua, H. Wang, M. Kang, and F.-Y. Wang, “Forecasting Horticultural Products Price Using ARIMA Model and Neural Network Based on a Large-Scale Data Set Collected by Web Crawler,” IEEE Trans. Comput. Soc. Syst., vol. 6, pp. 547–553, 2019, [Online]. Available: https://api.semanticscholar.org/CorpusID:182311399

D. Anggreani, I. A. E. Zaeni, A. N. Handayani, H. Azis, and A. R. Manga, “Multivariate Data Model Prediction Analysis Using Backpropagation Neural Network Method,” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 239–243, 2021, doi: 10.1109/EIConCIT50028.2021.9431879.

C.-C. Chen, J.-H. Chang, F.-C. Lin, J.-C. Hung, C.-S. Lin, and Y.-H. Wang, “Comparison of Forcasting Ability between Backpropagation Network and ARIMA in the Prediction of Bitcoin Price,” in 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2019, pp. 1–2. doi: 10.1109/ISPACS48206.2019.8986297.

D. Zhang and S. Lou, “The application research of neural network and BP algorithm in stock price pattern classification and prediction,” Futur. Gener. Comput. Syst., vol. 115, pp. 872–879, 2021, doi: 10.1016/j.future.2020.10.009.

M. H. P. Swari, I. P. S. Handika, and I. K. S. Satwika, “Comparison of Simple Moving Average, Single and Modified Single Exponential Smoothing,” in 2021 IEEE 7th Information Technology International Seminar (ITIS), 2021, pp. 1–5. doi: 10.1109/ITIS53497.2021.9791516.

J. Martinsson, A. Schliep, B. Eliasson, and O. Mogren, “Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networks,” J. Healthc. Informatics Res., vol. 4, no. 1, pp. 1–18, 2020, doi: 10.1007/s41666-019-00059-y.

B. Abbasi, T. Babaei, Z. Hosseinifard, K. Smith-Miles, and M. Dehghani, “Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management,” Comput. Oper. Res., vol. 119, p. 104941, 2020, doi: 10.1016/j.cor.2020.104941.

H. Shih and S. Rajendran, “Comparison of Time Series Methods and Machine Learning Algorithms for Forecasting Taiwan Blood Services Foundation’s Blood Supply,” J. Healthc. Eng., vol. 2019, 2019, doi: 10.1155/2019/6123745.

T. Herlambang, A. S. Kamil, and A. K. Devi, “Peralaman Persediaan Darah Jenis Whole Blood (WB) di PMI Kota Surabaya Dengan Neural Network,” J. Sist. Inf. Univ. Nahdlatul Ulama Surabaya, vol. 01, no. 01, pp. 39–44, 2019.

A. K. Devi and T.- Herlambang, “Peramalan Kebutuhan Darah Jenis Packet Red Cells (PRC) di PMI Kota Surabaya dengan Metode Jaringan Syaraf Tiruan Propagasi Balik,” Zeta - Math J., vol. 4, no. 1, pp. 7–11, 2018, doi: 10.31102/zeta.2018.4.1.7-11.

E. Prasiwiningrum, “Pengendalian Persediaan Darah untuk Pasien dengan Hemoglobin Rendah Menggunakan Metode Backpropagation,” J. Inform. Ekon. Bisnis, vol. 4, pp. 113–118, 2022, doi: 10.37034/infeb.v4i3.153.

A. C. N. Sirait, E. Buulolo, and H. Hutabarat, “Memprediksi Jumlah Penerimaan Dan Permintaan Darah Di Palang Merah Indonesia (Pmi) Kita Medan Dengan Menggunakan Metode Id3 (Studi Kasus: Pmi Kota Medan),” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 15–19, 2019, doi: 10.30865/komik.v3i1.1560.

S. Khalilpourazari, S. Soltanzadeh, G.-W. Weber, and S. K. Roy, “Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study,” Ann. Oper. Res., vol. 289, no. 1, pp. 123–152, 2020, doi: 10.1007/s10479-019-03437-2.

S. M. Hosseini-Motlagh, M. R. G. Samani, and S. Cheraghi, “Robust and stable flexible blood supply chain network design under motivational initiatives,” Socioecon. Plann. Sci., vol. 70, no. June, p. 100725, 2020, doi: 10.1016/j.seps.2019.07.001.

R. Ramezanian and Z. Behboodi, “Blood supply chain network design under uncertainties in supply and demand considering social aspects,” Transp. Res. Part E Logist. Transp. Rev., vol. 104, pp. 69–82, 2017, doi: https://doi.org/10.1016/j.tre.2017.06.004.

B. Hamdan and A. Diabat, “Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation,” Transp. Res. Part E Logist. Transp. Rev., vol. 134, no. August 2019, p. 101764, 2020, doi: 10.1016/j.tre.2019.08.005.

B. Fahimnia, A. Jabbarzadeh, A. Ghavamifar, and M. Bell, “Supply chain design for efficient and effective blood supply in disasters,” Int. J. Prod. Econ., vol. 183, pp. 700–709, 2017, doi: https://doi.org/10.1016/j.ijpe.2015.11.007.

M. R. G. Samani and S.-M. Hosseini-Motlagh, “Collaborative activities for matching supply and demand in the platelet network,” Expert Syst. Appl., vol. 231, p. 120629, 2023, doi: https://doi.org/10.1016/j.eswa.2023.120629.

I. Y. Sari and F. Maulana, “Simulasi Terbaik Dalam Persediaan Komponen Darah Menggunakan Metode Monte Carlo,” JITA (Journal Inf. Technol. …, vol. IV, no. 1, pp. 24–33, 2021.