TY - GEN
T1 - The Calculation of Player's and Non-Player Character's Gameplay Attribute Growth in Role-Playing Game with K-NN and Naive Bayes
AU - Widiyanto, Nur Rohman
AU - Nugroho, Supeno Mardi Susiki
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/17
Y1 - 2020/11/17
N2 - The game with the Role Playing Game (RPG) genre is a competitive game, between players against other players or enemies in the form of a Non-Player Character (NPC). Many game developers in making the game itself still use manual methods in determining gameplay attributes for player or enemy characters. In this research, several approaches such as k-NN, Normal Distribution, and Naive Bayes will be used in the program to calculate the growth of gameplay attributes automatically. The player attribute calculation distribution is done twice with the same inputs to find the maximum level limit of each attribute increase. It proves that the attribute increase and distribute to the levels are correspondents to the input variable. The enemies are in a normal distribution with the highest appearance probability between level 0 and 100. For enemy types, The input variables for enemy distribution by type fits with the output.
AB - The game with the Role Playing Game (RPG) genre is a competitive game, between players against other players or enemies in the form of a Non-Player Character (NPC). Many game developers in making the game itself still use manual methods in determining gameplay attributes for player or enemy characters. In this research, several approaches such as k-NN, Normal Distribution, and Naive Bayes will be used in the program to calculate the growth of gameplay attributes automatically. The player attribute calculation distribution is done twice with the same inputs to find the maximum level limit of each attribute increase. It proves that the attribute increase and distribute to the levels are correspondents to the input variable. The enemies are in a normal distribution with the highest appearance probability between level 0 and 100. For enemy types, The input variables for enemy distribution by type fits with the output.
KW - calculation
KW - gameplay attributes
KW - k-NN
KW - naive bayes
KW - role-playing game
UR - https://www.scopus.com/pages/publications/85099654302
U2 - 10.1109/CENIM51130.2020.9297945
DO - 10.1109/CENIM51130.2020.9297945
M3 - Conference contribution
AN - SCOPUS:85099654302
T3 - CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
SP - 103
EP - 110
BT - CENIM 2020 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Y2 - 17 November 2020 through 18 November 2020
ER -