Intelligent System for Recommending Study Level in English Language Course using CBR Method

Mirza Sutrisno, Utomo Budiyanto


In the admission process, an English Course uses a level placement test. The implementation of the test encountered some problems such as slow determination of student learning levels based on the results of paper based test that are still conventional. The purpose of this research provides the recommendations for an intelligent knowledgebased system in recommending student learning levels using the Case-Based Reasoning (CBR) method. CBR is one of the method that uses the Artificial Intelligence approach and focuses on solving problems based on knowledge from the previous cases, by calculating numerical local similarity and global similarity using the nearest neighbor algorithm as the basic for the technical development of this intelligent system. The result of the study was tested for the data accuracy with the confusion matrix method by the result 100% for the accuracy. For evaluating the system systematically was using the User Acceptance Test (UAT) method with the results of the evaluation is 88% of the system meets user needs and expectations


Recommended Study Level; Case Based Reasoning; English Course; Nearest Neighbor

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