• Patrisius Batarius(1)
    Universitas Katolik Widya Mandira
  • Alfry Aristo Jansen Sinlae(2*)
    Universitas Katolik Widya Mandira
  • (*) Corresponding Author
Keywords: DES, UTD-PMI Kupang, blood demand, blood supply, MAPE


There is a daily blood transfusion in the hospital. Blood management and distribution systems have been integrated into each UTD-PMI. Different systems were formed to maintain a balance between the demand and supply of blood bags. UTD-PMI Kota Kupang serves blood requests in 12 hospitals and 1 clinic located in the city and Kupang district. The demand for more blood than the supply of blood in the UTD-PMI is a personal problem. The prediction of demand and the blood supply becomes important so that there is no difference in demand and supply of blood in the very large UTD-PMI office. The study used the Double Exponential Smoothing (DES) method to predict blood supply and demand. Data from the last four years is used to predict the amount of blood demand and supply. Predictions with the DES method are divided into two categories. The first category uses the data of the last 3 years to predict data in the fourth year for the next 6 months. The training data used is divided into two types: the last 3 years and the last 2 years. Forecast results compared to real data in 2023. Category 2 uses data from the last four years to predict data for 2023. The training data used is divided into 4 types, each starting from the last 4, 3, 2, and 1 year. To determine the accuracy of the calculation using the mean absolute percentage error (MAPE). The results of determining training data with different ranges result in predictions of blood demand and blood supply, whose results are also different but still in the “good forecast” category.


Download data is not yet available.


[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] A. C. Adamuthe, R. A. Gage, and G. T. Thampi, “Forecasting cloud computing using double exponential smoothing methods,” in ICACCS 2015 - Proceedings of the 2nd International Conference on Advanced Computing and Communication Systems, Institute of Electrical and Electronics Engineers Inc., Nov. 2015. doi: 10.1109/ICACCS.2015.7324108.
[9] M. Lukman et al., “Forecasting Product Selling Using Single Exponential Smoothing and Double Exponential Smoothing Methods,” IOP Conf. Ser. Mater. Sci. Eng., vol. 662, no. 3, p. 032031, Nov. 2019, doi: 10.1088/1757-899X/662/3/032031.
[10] NCSS and LLC, “Exponential Smoothing – Trend & Seasonal,”
[11] F. Liantoni and A. Agusti, “Forecasting Bitcoin using Double Exponential Smoothing Method Based on Mean Absolute Percentage Error,” JOIV Int. J. Informatics Vis., vol. 4, no. 2, pp. 91–95, May 2020, doi: 10.30630/JOIV.4.2.335.
[12] R. Anggrainingsih, A. Prabanuadhi, and S. P. Yohanes, “Forecasting the Number of Patients at RSUD Sukoharjo Using Double Exponential Smoothing Holt,” in Proceeding - 2018 International Conference on ICT for Rural Development: Rural Development through ICT: Concept, Design, and Implication, IC-ICTRuDEv 2018, Institute of Electrical and Electronics Engineers Inc., Jul. 2018, pp. 54–58. doi: 10.1109/ICICTR.2018.8706850.
[13] E. Hasmin and N. Aini, “Data Mining for Inventory Forecasting Using Double Exponential Smoothing Method,” 2020 2nd Int. Conf. Cybern. Intell. Syst. ICORIS 2020, Oct. 2020, doi: 10.1109/ICORIS50180.2020.9320765.
[14] R. Mumpuni, Sugiarto, and R. Alhakim, “Design and implementation of inventory forecasting system using double exponential smoothing method,” in Proceeding - 6th Information Technology International Seminar, ITIS 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 119–124. doi: 10.1109/ITIS50118.2020.9321038.
[15] S. Hansun, M. B. Kristanda, Subanar, C. R. Indrati, and T. Aryono, “Forecasting domestic tourist arrivals to Bali: H-WEMA approach,” in Proceedings of 2019 5th International Conference on New Media Studies, CONMEDIA 2019, Institute of Electrical and Electronics Engineers Inc., Oct. 2019, pp. 121–124. doi: 10.1109/CONMEDIA46929.2019.8981825.
[16] Ramadiani, R. Syahrani, I. F. Astuti, and Azainil, “Forecasting the number of airplane passengers uses the double and the triple exponential smoothing method,” J. Phys. Conf. Ser., vol. 1524, no. 1, p. 012051, Apr. 2020, doi: 10.1088/1742-6596/1524/1/012051.
[17] N. D. Saputra, A. Aziz, and B. Harjito, “Parameter optimization of Brown’s and Holt’s double exponential smoothing using golden section method for predicting Indonesian Crude Oil Price (ICP),” in Proceedings - 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering, ICITACEE 2016, Institute of Electrical and Electronics Engineers Inc., Apr. 2017, pp. 356–360. doi: 10.1109/ICITACEE.2016.7892471.
[18] G. Airlangga, A. Rachmat, and D. Lapihu, “Comparison of exponential smoothing and neural network method to forecast rice production in Indonesia,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 17, no. 3, pp. 1367–1375, Jun. 2019, doi: 10.12928/TELKOMNIKA.V17I3.11768.
[19] L. S. Trend and D. E. Smoothing, “Exponential Smoothing – Trend,” NCSS, LLC. All Rights Reserved., pp. 1–7.
[20] A. Seasonality and M. Seasonality, “Exponential Smoothing – Trend & Seasonal,” NCSS Statistical Software, pp. 1–8.

PlumX Metrics

How to Cite
P. Batarius and A. A. J. Sinlae, “PREDICTION OF BLOOD DEMAND AND SUPPLY: DOUBLE EXPONENTIAL SMOOTHING METHOD APPROACH”, jicon, vol. 12, no. 1, pp. 1-9, Feb. 2024.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.