I Gede Susrama Mas Diyasa, Mohammad Idhom, Ahmad Sofian Aris Saputra, DESHINTA ARROVA DEWI, Tresna Maulana Fahrudin
Aim/Purpose: This study aims to develop and evaluate an automated scoring model for Indonesian student answers that enhances objectivity, accuracy, and adaptability – addressing persistent challenges in manual assessment, such as subjectivity, inconsistency, time inefficiency, and the increasing grading workload faced by teachers.
Background: The proposed model combines GloVe word embeddings with ...
automated scoring, GloVe, LSTM, ROUGE score, TF-IDF, cosine similarity, natural language processing, deep learning, automated essay scoring, Indonesian language, education quality, innovation