Generating Licensure Examination Performance Models Using JRip Classifier: A Data Mining Application in Civil Engineering

Abstract

The purpose of this study is to create performance models for civil engineer licensure examinations using the JRip classifier. it identified the attributes that are significant to the response attribute; it generated prediction models using JRip classifiers of WEKA; and it determined how likely is a CE gradute to pass the CE licensure examination. The respondent was obtained from the CE graduate of Cavite State University Indang main campus whos took a CE board examination from November 2016 to May 2019. The results obtained indicated the significance of the subject AENG 65, as well as CENG 65B through CENG 130 in predicting the CE licensure examination. The CE graduate is predicted to fail if the grade of AENG 65 is greater than equal to 3 and CENG 135 is less than equal to 2.5 and If CENG 120A and MATH 21B is greater than equal to 2.75 and the CENG 106 is less than 1.75 it also further concluded that if DCEE27 is greater than equal to 2.5 and the CENG 22A is greater than equal to 3 and the grade of CENG 110A less than equal to 2.75 then the CE graduate will fail the Licensure exam