Naïve Bayes-based Student Graduation Prediction Model: Effectiveness and Implementation to Improve Timely Graduation

Abstract

Studies in an educational institution, when the lack of timely graduation of students in each batch and the number of students in each batch, causes an imbalance between incoming students and outgoing students and causes a decrease in accreditation from the campus, this should not continue to happen, the solution to dealing with this problem as an early detection of students who graduate on time is to predict the length of the student study period they have. Therefore, researchers will discuss the design of a prediction system for graduating on time using the Naïve Bayes method, to predict student graduation so that there is no imbalance of incoming and outgoing students. The construction of this system also uses the Naïve Bayes method and the CRISP-DM (Cross Industry Standard Process Data Mining) development method. In this case study, the Naïve Bayes method predicts data into 2, namely 1 (graduated on time) and 0 (did not graduate on time) by labeling the data used. In this model using 3247 data with the selection of features, namely semester achievement index 1 (ips1), ips2, ips3, ips4, ips5, semester credit units1 (credits1), credits2, credits3, credits4, credits5, semester credit units not passed 1 (skstidaklulus1), skstidaklulus2, skstidaklulus3, skstidaklulus4, skstidaklulus5 and labels. Using these feature variables results in model performance with 80% accuracy, with 80% accuracy it can be said that the model works well.