SOM-SIS approach to auto summary of clustering results on university academic performance

Abstract

The analysis of the performance of higher education academic quality in terms of student achievement, study period, and drop out rates is still an intensive study among researchers. Several clustering methods are often used to understand student and graduate groups, in influencing college performance. However, the conventional method has only arrived at the results of clustering, so it is difficult to interpret it as a support for academic decisions, especially in mapping the position of universities against other universities nationally. This article introduces a combination of techniques from self organizing map and technique for oorder preference by similarity to an ideal solution (SOM-SIS), an auto-summarizing technique from clustering results as well as mapping university academic performance. First, the academic performance indicators are grouped using the self organizing map (SOM) method and the results are concluded using the technique for order preference by similarity to an ideal solution (TOPSIS) approach. The SOM-SIS technique was tested using data from one of the universities in Indonesia. As a result, the SOM-SIS technique has a 100% compatibility rate with the higher education quality assurance system, through recommendations from three university experts.