Classification of the Period Undergraduate Study Using Back-propagation Neural Network

The period of student study is one of the indicators of the determinants of the quality of a college. Based on the standard assessment of college accreditation by BAN-PT, the period of study became one of the elements of assessment of accreditation forms. Universities have an important role to monitor the development of student studies. For that, universities are required to always evaluate the performance of students. One way of evaluation that can be done is to explore the knowledge of academic data that will affect student performance. By utilizing data mining on student academic data, universities can obtain useful information. This information which later can be used as a reference in making improvements to the performance of student studies. Several previous studies used data mining techniques to predict the study period of students and this study will analyze the factors that influence the duration of undergraduate studies and modeling of ANN with back-propagation training algorithms to classify the study period. The result of this research is The BPNN algorithm is suitable for the classification of undergraduate study periods with accuracy rates above 85%.