Dropout Detection Using Non-Academic Data

Abstract

The common problem in the university is the high dropout rate. The high dropout rate will have a bad impact on the university. Various studies have tried to determine the factors that influence the dropout. Almost all research focuses on academic factors of students as a determinant of potential dropouts. However, there are sometimes cases of dropout students who cannot be determined using academic factors. This raises the hypothesis that the potential dropout students can be determined from non-academic factors. There are 5 non-academic factors criteria that can be used as determinants of dropout, demography, social interaction, finance, motivation, and personal. These criteria give rise to 37 factors that are considered influential in determining the potential dropout. The factors processed into three phases are collecting data, preprocessing data, and modelling. The factor that are independent to other factors are the number of family, the interest in the future study, and the relationship with the lecturer. Based on the result of correlation test there are two factors had correlation, so the modelling done with two combination factors. The best model is using combination of factor the number of family and the relationship with the lecturer using Decision Tree with split criterion is Maximum Deviance Reduction and maximum split is 2 with time for training is 1.7386 seconds.