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All Journal PELS (Procedia of Engineering and Life Science)
Novi Prastyanda Putra Pratama
Universitas Muhammadiyah Sidoarjo
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Forecasting the Amount of Blood Storage Using the Support Vector Machine (Svm) Method Novi Prastyanda Putra Pratama; Tedjo Sukmono
Procedia of Engineering and Life Science Vol 3 (2022): Proceedings of the 5th Seminar Nasional Sains 2022
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v3i0.1359

Abstract

Blood transfusion is a process of sending or transferring blood to another place and the task is delegated to the PMI Blood Donation Unit with several tasks including; deployment and preservation of blood donors, provision and processing of blood, and distribution of blood to health agencies. However, the supply and demand from health agencies have a significant difference. The difference for each blood group is very large, in group O deficiency by 28%, in group A deficiency by 38%, in group B excess by 28%, and in group AB deficiency by 84%. To overcome this problem, it is necessary to estimate the demand for blood that will occur in the future period. One of the tools that can answer this problem is demand forecasting and what will be used in this study is forecasting using the Support Vector Machine (SVM) method. This SVM method is a relatively new machine learning-based technique for making predictions, both used in classification and regression cases. The result of this study obtained good MAPE values, namely in blood group O is 14%, in blood group A is 15%, in blood group B is 13%, and in blood type AB is 24%.
Co-Authors Tedjo Sukmono