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All Journal JEITECH (JOURNAL OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY)
Saefurrasyid , Yusril
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Peramalan Permintaan Darah Menggunakan Backpropagation Neural Network di UTD PMI Kabupaten Lombok Barat | Budiman, Djul Fikry; Sasongko, Sudi Mariyanto Al; Saefurrasyid , Yusril
JEITECH (JOURNAL OF ELECTRICAL ENGINEERING, INFORMATION TECHNOLOGY, CONTROL ENGINEERING, AND ROBOTIC) Vol. 3 No. 1 (2025): Edisi April 2025
Publisher : Depertment of Electrical Engineering University of Mataram

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Abstract

In recent years, it noted that the number of requests for blood from hospitals in West Lombok and Mataram exceeded the bloodstock in the PMI Blood Transfusion Unit (UTD) in West Lombok Regency. The blood requests need predicting so there is no shortage of bloodstock. The model of the Backpropagation artificial neural network method is used to estimate the number of short-term blood requests. Predictive performance is analyzed based on the accuracy value based on the Absolute Maximum Percent Error (MAPE). The use of backpropagation neural networks can help respond to emergencies faster and predict more accurately. Accuracy rate prediction results for some type of blood component are: Whole Blood Bank Blood Hospital (WB BDRS) of 22%, BDRS Packet Red Cell blood of 78%, Thrombocyte Concentrate BDRS blood of 84 %, Fresh Frozen Plasma BDRS blood of 80%, WB NON-BDRS blood of 18%, PRC NON-BDRS blood of 66%, TC NON-BDRS blood of 55%, and FFP NON-BDRS blood of 47%. Based on these results, the JST backpropagation method can be used to predict TC BDRS blood and FFP BDRS blood with a good category.
Peramalan Permintaan Darah Menggunakan Backpropagation Neural Network di UTD PMI Kabupaten Lombok Barat | Budiman, Djul Fikry; Sasongko, Sudi Mariyanto Al; Saefurrasyid , Yusril
JEITECH (JOURNAL OF ELECTRICAL ENGINEERING, INFORMATION TECHNOLOGY, CONTROL ENGINEERING, AND ROBOTIC) Vol. 3 No. 1 (2025): Edisi April 2025
Publisher : Depertment of Electrical Engineering University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In recent years, it noted that the number of requests for blood from hospitals in West Lombok and Mataram exceeded the bloodstock in the PMI Blood Transfusion Unit (UTD) in West Lombok Regency. The blood requests need predicting so there is no shortage of bloodstock. The model of the Backpropagation artificial neural network method is used to estimate the number of short-term blood requests. Predictive performance is analyzed based on the accuracy value based on the Absolute Maximum Percent Error (MAPE). The use of backpropagation neural networks can help respond to emergencies faster and predict more accurately. Accuracy rate prediction results for some type of blood component are: Whole Blood Bank Blood Hospital (WB BDRS) of 22%, BDRS Packet Red Cell blood of 78%, Thrombocyte Concentrate BDRS blood of 84 %, Fresh Frozen Plasma BDRS blood of 80%, WB NON-BDRS blood of 18%, PRC NON-BDRS blood of 66%, TC NON-BDRS blood of 55%, and FFP NON-BDRS blood of 47%. Based on these results, the JST backpropagation method can be used to predict TC BDRS blood and FFP BDRS blood with a good category.
Co-Authors Djul Fikry Budiman, Djul Fikry Sudi Mariyanto Al Sasongko