Predictive Modeling of the First Year Evaluation Based on Demographics Data: Case Study Students of Telkom University, Indonesia

Student academic failure prediction is still interesting topics in the Educational Data Mining. One of the challenges is how to predict student academic failure as early as possible. This research focuses on predictive modeling of unsuccessful students in the first year evaluation. We propose a new concept of predictive modeling of the first year evaluation which combines 3 input data: demographics, academic and social media. The modeling can be divided into two sub modeling (normal period and extra period). In this paper, we focus on demographic data modeling (first sub-modeling) which correlated with the probability of a student to pass the first year evaluation on normal period. A Weka tool is used to get a pattern of data by using white box classifier (decision tree and rule base). Meanwhile, to solve the problem of unbalanced in our training data, we use data balancing scenario using same portion oversampling, random oversampling and SMOTE. From the testing result, we choose the best three student failure pattern of the F-Measure minor class value which obtained from "One R" and "ADTree" algorithms using Balancing scenario, the reason is because F-Measure describes the smallest error rate both FP (False Positive) and also FN (False Negative). From the best three of student failure pattern, we found that gender, selection path, study program and age are the attributes that are most correlated with the probability to pass the first year evaluation on extra period.