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Dea Dwi Rizki Tampubolon
STIKOM Tunas Bangsa, Pematangsiantar
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Penerapan Jaringan Saraf Tiruan Backprogation Dalam Memprediksi Jumlah Pasien Rumah Sakit Dea Dwi Rizki Tampubolon; Irfan Sudahri Damanik; Harly Okprana
Journal of Informatics, Electrical and Electronics Engineering Vol. 1 No. 2 (2021): Desember 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Artificial Neural Network is one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Artificial Neural Network (ANN) is defined as an information processing system that has characteristics similar to human neural networks. ANN is an information processing system that has similar characteristics to a biological neural network. The hospital is an integral part of a social and health organization with the function of providing services, healing disease and preventing disease to the community. Backpropagation network is one of the algorithms that are often used in solving problems. complicated problem. This algorithm is also used in regulatory applications because the training process is based on a simple relationship. The problems that occur at the Djasemen Saragih Pematangsiantar Hospital are the lack of doctors working at the hospital so that there is a density of patients that occur every year, and the absence of patient rooms that are placed at home. ill when there was an increase that was not recognized by the hospital. With the data available every year, it is expected that the use of artificial neural networks using the backprogation method is very useful for the hospital in determining the prediction of the number of hospital patients for the next year can be used as the basic material for changes or additional patient rooms when there is an excess of predicted patients.
Co-Authors Irfan Sudahri Damanik Okprana, Harly