<![CDATA[In RPG Games there are 3 most common professions namely Fighter, Mage, and Cleric. Of the three professions, there are complex differences that can affect the game RPG. To conduct research on the balance of the profession on Game RPG, the experiment takes a long time and high cost. Therefore, a method that reduces the cost of performing the testing ability on the RPG character professions. Therefore, this study discusses how we apply a commonly usable algorithm that is Particle swarm optimization (PSO) in character testing of characters in RPG Games automatically, thereby reducing the cost of game development. This research produces an AI bot that can mimic human behavior in general that matches the rules in the Game so as to help Game designers in determining the balancing ability of the Game they create. This study discusses the use of the Artificial neural network (ANN) to control a character in a game in order to simulate a match as a balanced size of a battle. The ANN controllers were trained without being taught or self-taught to understand their enemy's movements and were evaluated to serve as the primary controllers of the study. ANN own learning method using PSO to determine the best controller in the training. The research was conducted on turn-based games - RPG. The result of this research is a new balancing skill set process along with PSO configuration that influences this research.]]>
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