Mining significant association rules from on information and system quality of indonesian e-government dataset

Abstract

Electronic government (e-government) refers to how to apply the information and communication technologies (ICT) to improve the efficiency, effectiveness, transparency and responsibility of public governments. They are usually adopted in complex setting influenced not only the infrastructure factor but also the others factor such as end user satisfaction. Information and system quality are often seen as a key antecedent of user satisfaction. This paper presents an application of data mining technique based on association rules mining to capture interesting rules on information and system quality of Indonesian e-Government dataset. It is based on Least Frequent Items method by embedding FP-Growth algorithm. The rules are formed by implementing the relationship of an item or many items to an item (cardinality: many-to-one). The rule is categorized as interesting if it has a highest critical relative support, positive correlation and confidence. The results show that the total number of significant rules is 256 which is 14% from the overall rules captured i.e. 1811 on information quality data, meanwhile for system quality the total number of significant rules is 1790 which is 21% from the overall rules captured i.e. 18414.