Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Herlambang Yudha Prasetya
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Penerapan Neural Network untuk NPC Braking Decision pada Racing Game Herlambang Yudha Prasetya; Muhammad Aminul Akbar; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.76 KB)

Abstract

The popularity of the racing game genre is still visible today. Factors that supporting the popularity of this genre are speed driving which provides an exciting experience, interesting track variations, stunning graphics, and unique challenges presented by artificial intelligence. An important factor to be developed and in line with the core of the racing game that provides a fun challenge is artificial intelligence. Artificial intelligence behavior that is not varied and easy to guess, or even playing badly will affect the fun of the challenge of racing games. To avoid this, artificial intelligence is needed which is able to learn mindset and imitate human decisions when playing, especially braking decision or gas and brake determination. That is the basis of the Neural Network algorithm implemented for Artificial Intelligence in the Racing Game Starter Kit. The complexity of the code on the machine is simplified by changing the decision process to some intelligent neural networks that are similar to human neurons, especially how it works. Coupled with the adaptation process in a dynamic environment makes this algorithm interesting for AI researchers. By utilizing Cross-Validation, learning this algorithm with human behavior has a similarity rate of 76 percent. In a 10-round trial, the time results showed 12% or 72 seconds faster than the kit's AI, and a stable frame rate with an average of 59 frames per second.
Co-Authors Issa Arwani Muhammad Aminul Akbar