Criteria Redesign for Student Loan Consideration Using Factor Analysis and Data Clustering Approach
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Abstract
This paper presents the importance of redesigning the student loan consideration criteria which had been revealed to have some fault in evaluating the candidates. The historical data of student loan candidates elicited from their application form in the 2016 academic year was collected and analyzed by using Factor Analysis. There are 507 samples with 17 information attributes. The factor analysis reduced the dimensions of the variance in the samples by identifying the discriminative factors for student loan consideration. The experimental result shows that only nine factors were identified as discriminative factors, which are 1) Part-time job taken by the student, 2) Other scholarships that the student had been receiving, 3) Father’s salary, 4) Family ownership of the land, 5) House rental expense, 6) Number of siblings in the family, 7) Number of siblings currently studying, 8) Amount of money that the student get from other scholarships, and 9) Parental Marital Status. The clustering technique was used to measure the group of important factors reduced from the factor analysis. The clustering result showed that the clusters are obviously separated from each other. Therefore, these discriminative factors were elicited by using factor analysis which can be used to reconstruct the student loan consideration criteria and implement a decision support system.
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