Sensa Gudya Sauma Syahra
Master Program of Computer Science, FMIPA UGM, Yogyakarta

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The Effect of Text Summarization in Essay Scoring (Case Study: Teach on E-Learning) Sensa Gudya Sauma Syahra; Yunita Sari; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 1 (2022): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.69906

Abstract

The development of automated essay scoring (AES) in the neural network (NN) approach has eliminated feature engineering. However, feature engineering is still needed, moreover, data with labels in the form of rubric scores, which are complementary to AES holistic scores, are still rarely found. In general, data without labels/scores is found more. However, unsupervised AES research has not progressed with the more common use of publicly labeled data. Based on the case studies adopted in the research, automatic text summarization (ATS) was used as a feature engineering model of AES and readability index as the definition of rubric values for data without labels.This research focuses on developing AES by implementing ATS results on SOM and HDBSCAN. The data used in this research are 403 documents of TEACH ON E-learning essays. Data is represented in the form of a combination of word vectors and a readability index. Based on the tests and measurements carried out, it was concluded that AES with ATS implementation had no good potential for the assessment of TEACH ON essays in increasing the silhouette score. The model produces the best silhouette score of 0.727286113 with original essay data.