“…From the data we have collected, we selected and the results are, see Table 3. ), second we transform the data type, for data whose string type is changed to numeric, The result is, education / x1 (ELEMENTARY, JUNIOR HIGH, HIGH SCHOOL, S1) changed to numerical data of ordinal type (1,2,3,4), age /x2 was grouped into (<=25 years <-> "young", >25 years to < = 35 years <-> "medium", > 35 years <-> "old") from the grouping was changed later to numerical data with ordinal type (1,2,3), work experience / x3 was grouped into (0 to 1 year "New", >1 to <= 5 "moderate" years, and > 5 years, is experienced) the grouping results are changed into numerical form with ordinal type (1,2,3), sex/x4 (L/P) to (1,2). The results of user ratings are averaged to (leadership / x5, discipline / x6 and knowledge / x7), for training / x8 in the group to (0 = "no" and 1 = "yes"), knowledge of the average speed of the car when traveling / x9 and knowledge of vehicle conditions / x10 in the group to (0 = "do not know," 1 = " know), the results of the transformation into crisp values can be seen in Table 4.…”