Text Representation Method Analysis and Its Implementation in Automatic Essay Scoring System - Alya Zakhira Anjani; Divi Galih Prasetyo Putri; Widhy Hayuhardhika Nugraha Putra (CV Cognispectra Publishing, 2025)
The automatic essay scoring system is one of many problems in terms of natural language processing (NLP) that has long been studied. This study used an approach using text similarity with cosine similarity method to determine correct and incorrect predictions in an automatic essay scoring system. However, the text representation phase is also an important phase. This study compares the performance of three text representation methods in their implementation into an automatic essay scoring system. The methods are Indonesian Version of Bidirectional Encoder from Transformers (IndoBERT), Embeddings from Language Model (ELMo), and FastText. In addition, the combination of each method with WordNet as an additional lexical resource is also compared. The result of comparison using dataset “Indonesian Query Answering Dataset for Online Essay Test System” shows that the combination of IndoBERT and WordNet model has the best performance proven with highest accuracy achieved being 0.69, precision being 0.54, recall being 0.81, and F1-score being 0.48. Then the model was implemented as an essay evaluation feature development for the Certified Government Accounting Associate (CGAA) Exam Simulation site. The feature performance test results show an average load time of 418.8 ms when accessed by 10 users simultaneously and 15064 ms when accessed by 100 users simultaneously. The features developed are expected to be able to support the evaluation process more efficiently.