Data mining to prediction student achievement based on motivation, learning and emotional intelligence in MAN 1 Ketapang

The problems that exist in the school decline in student achievement ahead of class III, especially before approaching the national exam. If the learning achievement of third-grade students can be known earlier then the school can perform the actions necessary for students to achieve good learning achievement. This research uses two methods of data mining, Neural Network Model Multilayer Perceptron, and Decision Tree. For comparison, this study also uses t-statistic test, t-test and to compare precision/recall using Roc Curve. Neural Network Model Multilayer Perceptron Positive performance vector accuracy: 88.64% and Negative: 14.07%, precision (positive guidance class) positive 88.00% and negative 16.88%, recall (class: Ordinary guidance) positive 84.50%, and negative 21.73%. Decision Tree Positive performance vector accuracy: 84.82% and Negative: 15.24%, precision (positive guidance class) positive 86.55% and negative 18.52%, recall (class: ordinary guidance) positive 84.00% and negative 23.85%. Experiments conducted in this study aims to prove that data mining can predict student achievement by finding the best data mining method between the multilayer perceptron neural network and Decision tree to be implemented into integrated information system between student motivation data, student learning interest, and intelligence emotional students.