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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Muadz Askarul Muslim
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Penerapan Procedural Content Generation untuk Perancangan Level pada 2D Endless Runner Game menggunakan Genetic Algorithm Muadz Askarul Muslim; Eriq Muhammad Adams Jonemaro; Muhammad Aminul Akbar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

With the rapid development of the gaming industry, the amount of content needed in the game continues to increase. Increasing the amount of content is needed to keep players interested, so design work is increasingly needed to meet these requirements. Procedural Content Generation is a solution to save time and money and has been successfully implemented in several Endless Runner Games. Here the author uses the Genetic Algorithm method to implement the Procedural Content Generation on 2D Endless Runner Game. The author's Geographical Algorithm chooses because the Algorithm can optimize which is suitable for many cases of an environment. In addition to optimization, the Genetic Algorithm is modular, so it is separate from the application and can be applied to other cases without significant changes in it. Making levels can be done by using a random technique. But the results of the randomly obtained level can have problems such as the inappropriate results desired because there are no criteria as a measure of appropriateness from the results that are made randomly as can be passed the level that has been made. Whereas in Genetic Algorithm there is a section that can select each individual and population to fit the specified criteria. The results of the tests show the time needed for the program to make a level very short, which is 0.02 seconds. From these results show that the algorithm can be applied and works well in the creation of levels. The resulting level can also be skipped by players based on the results of testing by a sample of players. But the difficulty of the level produced cannot be controlled using the Genetic Algorithm used.
Co-Authors Eriq Muhammad Adams Jonemaro Muhammad Aminul Akbar