Maximum Attribute Relative Approach of Soft Set Theory in Selecting Cluster Attribute of Electronic Government Data Set

Electronic government (e-government) is the use of information and communication technology to provide information and services for the citizen. Many researchers argue that it is very important to know what the dominant variable influences citizen in using e-government. A number studies have used empirical approach to know the variables, but very rarely use other technique such as data mining. One of the powerful data mining technique is Maximum Attribute Relative (MAR), the technique is based on a soft set theory by introducing the concept of the attribute relative in information systems. Therefore, we present the applicability of MAR for clustering attribute selection. The real data set is taken from a survey at Bandung District in Indonesia. A total 200 participants have jointed in this survey. Most respondents are female i.e. 105 persons and the rest are male i.e. 95 persons with alpha score yielded 0.846. At this stage of the research, we show how MAR can be used to select the best clustering and found that Facilitating Condition (FC) is the highest variable of the citizen behavior in adopting e-government service. Furthermore, the result also may potentially contribute to decision maker how to design a good e-government in order to reduce bureaucracy and further to improve public services.