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Journal : Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi

Implementation and Impact of Virtual Reality on Survival Horror Games Armanto, Hendrawan; Anthony, Gregorian; Pickerling, Pickerling
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2021)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.665 KB) | DOI: 10.25139/inform.v6i2.3943

Abstract

Currently, games are an important part of human life. Not only serves as entertainment, games nowadays also serve as education, communication, socialization, and even a job for some people. This makes technology in the game world more developed and closer to reality. One of the popular and interesting technologies is virtual reality. This technology has various elements, but the most important element is the immersion element, which can give users the sensation to feel as if they are in a real environment. In this research, the authors examine the effects of virtual reality when combined with horror games. The selection of horror games was made because this genre is one of the genres with the fastest immersion element compared to other game genres. In addition to the use of virtual reality technology, considering that horror games require complex particle simulations and good lighting, the authors use the Unreal Engine as the main engine in this game. The test method used in this study is the beta testing method with the assessment using the user acceptance method. The conclusion was that virtual reality technology combined with the advantages of the unreal engine caused game players to get a tenser atmosphere.
MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem Armanto, Hendrawan; Dwi Putra, Ronal; Pickerling, Pickerling
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 7 No. 1 (2022)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v7i1.4381

Abstract

Tetris is one of those games that looks simple and easy to play. Although it seems simple, this game requires strategy and continuous practice to get the best score. This is also what makes Tetris often used as research material, especially research in artificial intelligence. These various studies have been carried out. Starting from applying state-space to reinforcement learning, one of the biggest obstacles of these studies is time. It takes a long to train artificial intelligence to play like a Tetris game expert. Seeing this, in this study, apply the Genetic Algorithms (GA) and the most valuable player (MVPA) algorithm to optimize state-space training so that artificial intelligence (agents) can play like an expert. The optimization means in this research is to find the best weight in the state space with the minimum possible training time to play Tetris with the highest possible value. The experiment results show that GAs and MVPA are very effective in optimizing the state space in the Tetris game. The MVPA algorithm is also faster in finding solutions. The resulting state space weight can also get a higher value than the GA (MVPA value is 249 million, while the GA value is 68 million).
MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem Hendrawan Armanto; Ronal Dwi Putra; C. Pickerling
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 7 No. 1 (2022)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (854.14 KB) | DOI: 10.25139/inform.v7i1.4457

Abstract

Tetris is one of those games that looks simple and easy to play. Although it seems simple, this game requires strategy and continuous practice to get the best score. This is also what makes Tetris often used as research material, especially research in artificial intelligence. These various studies have been carried out. Starting from applying state-space to reinforcement learning, one of the biggest obstacles of these studies is time. It takes a long to train artificial intelligence to play like a Tetris game expert. Seeing this, in this study, apply the Genetic Algorithms (GA) and the most valuable player (MVPA) algorithm to optimize state-space training so that artificial intelligence (agents) can play like an expert. The optimization means in this research is to find the best weight in the state space with the minimum possible training time to play Tetris with the highest possible value. The experiment results show that GAs and MVPA are very effective in optimizing the state space in the Tetris game. The MVPA algorithm is also faster in finding solutions. The resulting state space weight can also get a higher value than the GA (MVPA value is 249 million, while the GA value is 68 million).