<![CDATA[In this study the authors performed data classification prospectus franchise locations derived from franchise consultant MBC (mandiri business consultant) to determine the prospects of a location waralaba. From the data of 186 cases of existing franchise, there were 152 cases are prospects and 34 cases of the remaining are not prospects. This means that 18.27% of potential locations might, could not be maintained or changed into potential locations for the franchise. If possible franchise locations are not known earlier outlook, then the franchise consultant can perform the actions necessary to make the site a prospect. The data are analyzed and classified using the decision tree approach C4.5 algorithm. For the determination of attributes and variables involved in determining the location of the franchise prospectus, the author uses the rules and guidelines of a franchise consultant who has been posted. By enabling attributes or variables not the destination as many as 22 variables obtained by 98 rules. The rules or the rule will become a reference in determining the prospective franchise locations are categorized no outlook or outlook. Of the rules that form, there are two categories of rules. That is the rule that produces a particular classification, prospects and prospects as well as rules that the conclusion is not classified. Keyword: decision tree, C4.5 algorithm, prospectus franchise locations, database design]]>
Copyrights © 2011
