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    Implementasi Metode Algoritma Genetika dalam Mengoptimasi Penjadwalan Perawat pada Rumah Sakit Advent Medan

    Implementation of Genetic Algorithm Method in Optimizing Nurse Sheduling in Medan Advent Hospital

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    Date
    2024
    Author
    Hanafi, Bahri
    Advisor(s)
    Sawaluddin
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    Abstract
    Scheduling hospital nurses is something that must be considered in a hospital. Scheduling nurse work shifts is necessary to arrange working hours in such a way that the quality of nurses' work is well maintained and has a positive impact on the hospital when serving patients. According to observations made at Medan Adventist Hospital, nurses' work shift arrangements experienced uneven shift scheduling for each nurse and the number of nurses shift work was not fulfilled in accordance with applicable hospital regulations. The aim of this research is to implement the genetic algorithm method to solve scheduling problems. The genetic algorithm is a heuristic method where the surviving chromosomes are strong chromosomes, while weak chromosomes will become extinct, the aim is to obtain optimal parameter values for each nursing schedule. The variables used in this research are the nurse's name, length of shift working hours and number of days the nurse works. There are two test components carried out in scheduling nurses to obtain optimal parameters, including testing the crossover rate and mutation rate values with the highest fitness value of 1. The results of scheduling using the genetic algorithm have increased the number of nurses' working hours by 9% and meet hospital regulations. . From this it can be concluded that the application of genetic algorithms to optimize nurse scheduling gets the best solution.
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    https://repositori.usu.ac.id/handle/123456789/94738
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    Repositori Institusi Universitas Sumatera Utara (RI-USU)
    Universitas Sumatera Utara | Perpustakaan | Resource Guide | Katalog Perpustakaan
    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV