Application of Artificial Intelligence to the Development of Playing Ability in the Valorant Game

Abstract

From children to adults, everyone enjoys playing online games as a form of entertainment. Online games have a more popular market because they can meet other players worldwide connected to the internet. NPCs (players controlled by a computer system) are also available in online games as player substitutes or for skill practice. As a result, we have been interacting with artificial intelligence in the competition and our environment without realizing it. The game's AI (Artificial Intelligent) can offer an experience similar to playing with other players. Artificial intelligence may always influence online games, whether to replace opponents or choose the game's plot. First Person Shooter (FPS) game Valorant was made available for free on the Windows platform by Riot Games. With a CS: GO-like concept (Counter Strike Global Offensive). The amount of money you have available for each round must be calculated at the equipment purchasing phase to produce effective gaming equipment. The Greedy Algorithm is one of the algorithms that can be used to choose the equipment for the money/cred owned. This algorithm will select the most expensive item the player can buy each round so that the player's chances of winning that round increase.

Pengaruh Sensation Seeking terhadap Internet Gaming Disorder pada Gamers Usia 16-24 Tahun

“…Valorant merupakan game dengan genre First Person Shooter (FPS) yang dikembangkan oleh Riot Games untuk sistem operasi Windows. permainan ini melibatkan dua tim yang terdiri dari lima pemain masing-masing dan setiap pemain diharuskan untuk memilih agen Valorant yang memiliki keterampilan unik yang menambah keasyikan dalam pertempuran [9]. Permainan ini juga menawarkan berbagai pilihan agen, senjata, mode permainan, dan peta yang dapat dimainkan.…”

Comparative Analysis of the Performance of the Decision Tree and K-Nearest Neighbors Methods in Classifying Coffee Leaf Diseases

“…Purnamawati et al in 2020 conducted research to detect leaf diseases in rice plants using the decision tree algorithm with 100% accuracy results and using the KNN algorithm with 84% accuracy results [5]. Kusuma et al in 2022 also conducted research on the classification of leaf diseases in corn plants using several algorithms, one of the algorithms used was k-nearest neighbor (KNN) which obtained accuracy results of 93.8%, precision of 93.9%, and recall 93.8% [6]. Wahyuningtyas et al in 2022 conducted research on disease identification on coffee leaves using the Local Binary Pattern and Random Forest methods.…”