No abstract

-A rule-based system is a system based on the set of rules used to make inference knowledge. The system gathers knowledge into the representation of knowledge in the form of a rule. However, the knowledge in the form of the rule is inductive, meaning that the algorithm can construct the rule by studying a limited number of cases and then the induced rule of a limited number of cases and then generalize it to the general reality from time to time. This, of course, has the degree of inaccuracy in expressing reality into knowledge, or an experienced expert builds it but it is not absolute that the knowledge it possesses is 100% accurate or always consistently true from one time-space location to another time-space location. Therefore, the need for a formula that can measure the quality of the resulting rule and assess the consistency of the rule. In this study, we did a review of the ideas of people trying to measure knowledge built inductively by either the algorithm or the experts. These measurements are based on several parameters defined by them according to the underlying assumptions. This review seeks to partially present how ideas to measure the rule as knowledge representation from a varied viewpoint and how people construct evaluation models to assess the resulting regulations either from the experts or human experts as well as those resulting from the induction rule algorithm much developed.

Abstract. This theory attempted to explore the possibility of using regular language further in image analysis, departing from the use of string to represent the region in the image. But we are not trying to show an alternative idea about how to generate a string region, where there are many different ways how the image or region produces strings representing, in this paper we propose a way how to generate regular language or group of languages which performs both classify the set of strings generated by a group of a number of image regions. Researchers began by showing a proof that there is always a regular language that accepts a set of strings that produced the image, and then use the language to perform the classification. Research then expanded to the pixel level, on whether the regular language can be used for clustering pixels in the image, the researchers propose a systematic solution of this question. As a tool used to explore regular language is deterministic finite automata. On the end part before conclusion of this paper, we add revision version of this theory. There is another point of view to revision version, added for make this method more precision and more powerfull from before.