Abstract
University requires the integration of data from one system with other systems as needed. This is because there are still many processes to input the same data but with different information systems. The application of data integration generally has several obstacles, one of which is due to the diversity of databases used by each information system. Schema matching is one method that can be used to overcome data integration problems caused by database diversity. The schema matching method used in this research is linguistic and constraint. The results of the matching scheme are used as material for optimizing data integration at the database level. The optimization process shows a change in the number of tables and attributes in the database that is a decrease in the number of tables by 13 tables and 492 attributes. The changes were caused by some tables and attributes were omitted and normalized. This research shows that after optimization, data integration becomes better because the data was connected and used by other systems has increased by 46.67% from the previous amount. This causes the same data entry on different systems can be reduced and also data inconsistencies caused by duplication of data on different systems can be minimized.
Highlights
The use of information technology currently plays an important role in the success of an organization
The data used in this research are databases at tertiary institutions such as administration, academic databases, KPKKNTA, ABDIMAS, and others
The results showed that there were 16 pairs with TP value and 8 pairs with FP value while none had FN value. These results indicate that in data integration there is still duplication of data that can cause data inconsistencies. b
Summary
The use of information technology currently plays an important role in the success of an organization. In the application of data integration in general there are several obstacles, one of which is the diversity of database schemes used [8] The diversity of this scheme occurs because the amount of information stored in the database continues to grow, this causes the need for the information to be stored in several different databases, and database integration is a very important aspect in maintaining consistency between these databases on an ongoing basis [9]. Schema matching is one method that can be used to overcome data integration problems caused by database diversity. Research conducted by Sutanta and colleagues combines two-hybrid schema matching methods to overcome database integration problems caused by database diversity [15]. S: a symbol that represents a reference database element (Source) t: a symbol that represents the database element to be matched (Destination) The similarity tested using this linguistic method is the similarity based on the name of the table between the pair of tables from the source database with the destination
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