Observation of Imbalance Tracer Study Data for Graduates Employability Prediction in Indonesia

Abstract

Tracer Study is a mandatory aspect of accreditation assessment in Indonesia. The Indonesian Ministry of Education requires all Indonesia Universities to anually report graduate tracer study reports to the government. Tracer study is also needed by the University in evaluating the success of learning that has been applied to the curriculum. One of the things that need to be evaluated is the level of absorption of graduates into the working industry, so a machine learning model is needed to assist the University Officials in evaluating and understanding the character of its graduates, so that it can help determine curriculum policies. In this research, the researcher focuses on making a reliable machine learning model with a tracer study dataset format that has been determined by the Government of Indonesia. The dataset was obtained from the tracer study of Amikom University. In this study, SVM will be tested with several variants of the algorithm to handle imbalanced data. The study compared SMOTE, SMOTE-ENN, and SMOTE-Tomek combined with SVM to detect the employability of graduates. The test was carried out with K-Fold Cross Validation, with the highest accuracy and precision results produced by SMOTE-ENN SVM model by value of 0.96 and 0.89.