Effectiveness of Neuro-fuzzy Inference System for Predicting Player Character in the Balinese Serious Game Model

Abstract

Developing children's character must start from an early age. As children consider playing games a pleasant activity, exposing them to traditional games that involve cultural wisdom is an effective way to instill ethical norms and moral values in them. In Bali, many traditional games, such as Petaumpet and Tajog, require the children to be disciplined, cooperative, obedient and act honestly. Behaving in such a manner while playing can be an effective strategy for the character development of children. I Durma is one of the folklores that has been developed in the form of ICT-based game. This type of challenge game has an impact on the player's emotions. This study explains the relationship between game-factor, emotion, and character variables of game players. All three variables were obtained through questionnaire integrated in the game. The data obtained were analyzed using the neurofuzzy and backpropagation methods. The analysis result shows that there was a relationship between game-factor, emotional, and character variables. This is indicated by the output values of the neuro-fuzzy analysis. It was obtained that: training analysis (40.9 ± 3.41), testing analysis (42.0 ± 2.64), and checking analysis (42.4 ± 2.75) with the difference between them (∆µ < 5%) of 50 max scores. In other words, characters can be predicted from game-factor and players' emotion. The findings of this study can enable teachers to help students or users choose right games and control their emotions while playing as these can affect the character development of the children. We also show that children who were involved in playing the game responded positively to the game based on Balinese local wisdom.

Preferences of Acceptance for Gayo Arabica Coffee Based on Sensory Test Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

“…That was done because each membership function produces different levels of accuracy and prediction results errors [36][37][38]. Suwindra et al [39] said that data analysis to determine the relationship between variables begins with determining membership functions such as Generalized bell, Gaussianmf, Gaussian2mf, and Pimf. The same thing is also used by Mashaly & Alazba [40] in their research to develop the ANFIS method using 8 membership functions, where Gaussianmf, Gaussian2mf, and Pimf are 3 membership functions that have the best accuracy.…”