Classifying the Complexity of Competency in Elementary School based on Supervised Learners

Complexity of competency (CoC) expresses the difficulty level of a competency. The CoC is one of the important parameters for determining the minimum passing level of competency in an assessment system. In Indonesia, the value of CoC is defined by experts based on conditions of subject, students and teachers in each school. The definition process is determined subjectively, where different experts may evaluate CoC in different ways. This is a problem of data classification that requires an automated tool that copes with the amount of data and produces uniform results. To apply an intelligent classifier is essential to solve the issue. This study aims to find the best method for classifying the complexity of competency in Elementary School. Four supervised learning techniques, namely, Naïve Bayes, Multilayer Perceptron, Sequential Minimal Optimization, and RIPPER, were implemented to analyze the dataset. Based on an experiment with 203 data, we found that the Multilayer Perceptron achieved the best performance in the sense of Mean Absolute Error, Root Mean Squared Error, and Receiver Operating Characteristic value. At the same time SMO is better than all other methods in precision, recall, and F-Measure.