Predicting students achievement based on motivation in vocational school using data mining approach

Vocational is one of education types in Indonesia. Graduates from vocational school need to have enough motivation to get into working environment either as employees or as entrepreneurs. In vocational education, it is important to monitor students' motivation and achievement. It will help to understand students' condition and give an overview in setting the appropriate program for the students. This study aims to predict student's achievement in a vocational education based on their motivation. Classification as a data mining technique is used for the examining process. There are four variables used as classes consist of three variables describing students' needs and GPA as academic achievement indicator. Classification algorithm is done using naïve bayes algorithm based on parameters describing motivation level as the input. RMSE value obtained from the classification experiments is used to identify the relationship between students motivation and their achievement. Results show that the n-ACH variable has RMSE value 0.3696 and GPA variable has RMSE value 0.4049. Compared with the others, n-ACH is the most accurate variable predicted by motivation variables.