IMPLEMENTASI ALGORITMA FUZZY EVOLUSI PADA PENUGASAN KARYAWAN

    Tamara Widianti, - (2018) IMPLEMENTASI ALGORITMA FUZZY EVOLUSI PADA PENUGASAN KARYAWAN. S1 thesis, Universitas Pendidikan Indonesia.

    Abstract

    AlgoritmaFuzzyEvolusi (FE) merupakanperpaduanantaralogika fuzzy danalgoritmagenetika (GA).Algoritmafuzzyevolusimerupakanpengembangandarialgoritmagenetika. Cara kerjaalgoritmatersebutdidasarkanpada proses genetika yang adadalammakhlukhidupdandigabungdenganlogikafuzzy .Terdapatempat parameter yang digunakanpadaalgoritma FE yaituukuranpopulasi, banyaknyagenerasi, probabilitascrossover (ρc) dan probabilitas mutasi (ρm).Cara kerja algoritma FE dimulai dari pembangkitan populasi awal, representasi kromosom, menghitungnilaifitness, seleksi, hitungnilai parameter denganlogika fuzzy, lalucrossoverdanmutasi.Logikafuzzypadaalgoritma FE digunakanuntukmenentukannilai parameter ρc dan ρmdenganinputannyayaitu parameter ukuranpopulasidan parameter banyaknyagenerasi. AlgoritmaFuzzyEvolusidapatdigunakanuntukmenyelesaikanmasalahoptimasidiantaranya TSP (Travel Salesman Problem), masalahpenjadwalandanmasalahpenugasan. Padapenelitianinialgoritma FE diimplementasikanuntukpenyelesaianmasalahpenugasan di suatuperusahaanjasauntukmenentukan total gajikepadakaryawan yang minimum. Berdasarkanhasilimplementasidapatdisimpulkanbahwaalgoritma FE dapatmenyelesaikanmasalahpenugasankaryawandanmampumemberikansolusi yang cukupbaik;---Fuzzy Evolution Algorithm (FE) is a combination of fuzzy logic and genetic algorithm (GA). The workings of the algorithm are based on the genetic processes that exist in living things and are combined with logic. There are four parameters used in the FE algorithm, namely population size, number of generations, probability of crossover (ρc) and probability of mutation (ρm). The workings of FE algorithms start from the initial population generation, chromosome representation, calculate fitness values, selection, calculate parameter values with fuzzy logic, then crossover and mutation. Fuzzy logic in FE algorithm is used to determine the parameter value ρc and ρm with its input, which are parameters of population size and parameters of the number of generations. Fuzzy Evolution Algorithm can be used to solve optimization problems including TSP (Travel Salesman Problem), scheduling problems and assignment problems. In this research FE algorithm is implemented to solve assignment problems in a service company so that the total salary given to employees is as minimum as possible. Based on the results of the implementation it can be concluded that the FE algorithm can solve employee assignment problems and be able to provide a fairly good solution.

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    Official URL: http://www.repository.upi.edu
    Item Type: Thesis (S1)
    Additional Information: No. Panggil : S MAT TAM i-2018; Nama Pembingbing : I. Khusnul Novianingsih II. Entit Puspita; NIM : 1405638;
    Uncontrolled Keywords: AlgoritmaFuzzyEvolusi, LogikaFuzzy, AlgoritmaGenetika, PenugasanKaryawan, Solusi Optimal. Gaji Minimum, Fuzzy Evolution Algorithm, Fuzzy Logic, Genetic Algorithm, Employee Assignment, Optimal Solution. Minimum salary.
    Subjects: L Education > L Education (General)
    Divisions: Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Matematika - S1
    Depositing User: Ryan Taufiq Qurrohman
    Date Deposited: 14 Feb 2020 03:53
    Last Modified: 26 Mar 2022 14:16
    URI: http://repository.upi.edu/id/eprint/46728

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