## 4 Evaluation
### Results  
Most participants enjoyed playing the game based on the survey responses. Most participants (8 out of 10) rated their enjoyment of the game as 4 out of 5, \(-\)1 (less enjoyment) and 5 (most enjoyment); however, the remaining two participants rated their enjoyment as 3 out of 5. This result suggests that most players found the game enjoyable, but not everyone found it to be equally engaging. The participants' responses varied more regarding the difficulty of the game, with scores ranging from 4 to 9 on a 10-point scale, 1 (too easy) and 10 (too hard). Most participants rated the game as moderately challenging, with 50% of the players giving scores ranging from four to six. However, one participant scored nine, suggesting that they found the game challenging. This suggests that the game is relatively easy for most players. Regarding the self-perception of performance in the game, the results of the question were primarily negative, with 60% of players rating their performance below the average as \(<\) 5 out of 10 on the scale where one is very bad and ten is awesome. This result could indicate that the players found the game challenging and struggled to perform well. On the other hand, the remaining 40% rated their performance in a range from 6 to 10, indicating that they found the game more manageable than other players. The survey also validates the player's flow experience (see Figure 10). The results showed that playing Astro provided a flow experience to most participants, with 90% of the players reporting high levels of enjoyment, interest, and immersion in the game. Furthermore, all the participants felt in control of their actions and knew what to do during the gaming activity, indicating a high level of competence. Interestingly, the participants' responses were divided according to their feeling of time passing fast during the activity, with 70% agreeing and 30% disagreeing. Overall, this result suggests that the dynamic adjustment of game complexity in Astro provided a flow experience to most participants, which is a positive outcome in this research project because we validated the flow experience of the player. Finally, participants were asked about their self-perception of the dynamic adjustment of complexity. Seventy percent of the participants mentioned that they noticed some sort of complexity adjustment, while the remaining 30% did not. This result suggests that a significant majority of the participants noticed the dynamic adjustment of complexity in Astro, implying that the game provided a challenging and stimulating experience. Those who noticed the complexity adjustment were asked if they believed the adjustment affected their performance. Responses were on a scale of 1 to 10, where 1 was \"Very bad\" and 10 was \"Wonderful.\" A total of 29% believed that their performance was moderately affected, while 71% responded in the range of six to nine. This indicates that they perceived the dynamic adjustment of complexity in Astro to be favorable for their game experience, indicating that the game's adaptive difficulty was effective in enhancing their experience. This confirms that incorporating dynamic complexity adjustment in game design can improve player satisfaction and enjoyment, showing that the intelligent model successfully classifies players into skill levels and leads to improved player engagement and satisfaction. Four game sessions were randomly selected for analysis and discussion to demonstrate how the model adjusted difficulty and levels. Table 3 shows that the FNN model can effectively adjust the difficulty of a game session. In most cases, players advanced to higher levels, demonstrating that FNN achieved a balance between challenge and accessibility. In session 1, for example, the player started at Level 1 and advanced to Level 12. The model adjusted the difficulty so that the player experienced a progression of skill, ending up in the beginner category with an accuracy of 62.39%.