Fadika Zaidan Zahran, - and Siti Fatimah, - and Lukman, - (2025) PENYELESAIAN MODEL MULTIOBJEKTIF PADA MASALAH PEMILIHAN STARTING LINEUP KLUB AC MILAN PADA GAME eFOOTBALL MENGGUNAKAN ALGORITMA GENETIKA. S1 thesis, Universitas Pendidikan Indonesia.
Abstract
eFootball adalah salah satu video game sepak bola yang terkenal, menawarkan fitur strategis seperti pemilihan starting lineup untuk mengoptimalkan performa tim. Penelitian ini bertujuan untuk mengoptimalkan pemilihan starting lineup klub AC Milan pada game eFootball dengan menggunakan algoritma genetika berbasis model multiobjektif. Masalah ini dimodelkan menggunakan Weighted Sum Model, dengan dua tujuan utama yang diberikan bobot: memaksimalkan rating pemain dengan bobot 0,7 dan meminimalkan gaji pemain dengan bobot 0,3. Implementasi algoritma genetika melibatkan tahapan seperti representasi kromosom, inisialisasi populasi, seleksi, crossover, dan mutasi, yang dilakukan hingga mencapai batasan jumlah generasi. Implementasi dilakukan menggunakan bahasa pemrograman Python. Hasil menunjukkan bahwa algoritma genetika mampu menghasilkan total rating pemain sebesar 917 dengan total gaji sebesar 986.000 GP. Penelitian ini membuktikan bahwa algoritma genetika adalah pendekatan yang efektif dalam membantu pengambilan keputusan strategis untuk meningkatkan performa tim dalam game eFootball. eFootball is one of the most popular soccer video games, offering strategic features such as selecting a starting lineup to optimize team performance. This study aims to optimize the starting lineup selection for AC Milan in the eFootball game using a genetic algorithm based on a multi-objective model. The problem is modeled using the Weighted Sum Model with two main objectives assigned weights: maximizing player ratings with a weight of 0.7 and minimizing player salaries with a weight of 0.3. The genetic algorithm implementation involves stages such as chromosome representation, population initialization, selection, crossover, and mutation, performed until the generation limit is reached. The implementation was carried out using the Python programming language. The results show that the genetic algorithm successfully achieved a total player rating of 917 with a total salary of 986,000 GP. This study demonstrates that the genetic algorithm is an effective approach to assist strategic decision-making to enhance team performance in the eFootball game.
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| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | https://scholar.google.com/citations?view_op=new_profile&hl=en ID SINTA Dosen Pembimbing: Siti Fatimah: 5978161 Lukman: 6675529 |
| Uncontrolled Keywords: | eFootball, Metode Optimisasi, Model Multiobjektif, Weighted Sum Model, Algoritma Genetika eFootball, Optimization Methods, Multi-Objective Model, Weighted Sum Model, Genetic Algorithm |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Matematika - S1 > Program Studi Matematika (non kependidikan) |
| Depositing User: | Fadika Zaidan Zahran |
| Date Deposited: | 07 May 2025 01:33 |
| Last Modified: | 07 May 2025 01:33 |
| URI: | http://repository.upi.edu/id/eprint/133053 |
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