Automatic Essay Grading System Based on Latent Semantic Analysis with Learning Vector Quantization and Word Similarity Enhancement

Abstract

Department of Electrical Engineering Universitas Indonesia has developed an automatic essay grading system called Simple-O since 2007. Simple-O uses the Latent Semantic Analysis (LSA) method to compare two essays by extracting the essay into matrix. The previous development of Simple-O is the addition of Learning Vector Quantization (LVQ) which is a method of artificial neural network. This research will discuss and provide analysis related to the effect of adding word similarity function to the automatic essay grading system (Simple-O) to the accuracy of the system itself. The experiment will be conducted with five different scenarios by varying the number of keywords in the student's answer essay to 100%, 80%, 60%, 40%, and 20% of the reference essay keywords. According to the result, there are scenarios that has decreased and increased in accuracy. The average accuracy of the Simple-O system after the addition of word similarity function has increased, though not significant. The average increase in accuracy after the addition of word similarity function is 5.4% from 90.9% to 96.3%.