Implementasi Dynamic Difficulty Adjustment Pada Racing Game Menggunakan Metode Fuzzy - Reza Saputra; Muhammad Aminul Akbar; Tri Afirianto (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Some racing games have a feature to play the game competitively against other player or against Artificial Intelligence (AI) or commonly called as a bot. In its implementation, we tend to find that the ability between the player and the bot is far. This causes boredom if the player has much higher ability than the bot, and will produce anxiety if the player's ability is much lower when compared with the bot. In some racing games there is an option to choose the difficulty level before starting the game, but this feature is still considered less effective to balance the ability of player and bot, because the ability of players can increase as the time goes by, and new players tend to confused that they don't know what category of difficulty that suits their abitility. To solve the problem, the researcher will implement Dynamic Difficulty Adjustment (DDA) by using Fuzzy method that able to adjust the ability of the bot according to player's ability over time. DDA testing is done by playing and matching static bots with DDA bots. Test results show that DDA bots are able to adjust their behavior with the static bots ability, in which the output parameter value changes at 35 seconds, the output parameter values generated for caution angle, steer sensitivity, max wander distance, and wander rate are 41,83, 0.012, 3,57, and 0,03 respectively. The overall value of the parameter categorized as HARD.