Optimization of a FastText-Based BiLSTM Model with IndoBERT Semantic Data Augmentation for Indonesian Text Classification

Nur Fadilah, Bayu Anugerah Putra, Muh. Isbar Pratama — Mon, 30 Mar 2026 00:00:00 +0000

Cognitive assessment through short-answer essays requires a consistent and objective scoring process; however, manual evaluation often suffers from time constraints and inter-rater variability. Automatic Essay Scoring (AES) has emerged as a promising approach to automate the assessment process. This study proposes an optimized Bidirectional Long Short-Term Memory (BiLSTM) model combined with FastText embeddings for Indonesian text classification using semantically augmented data generated by IndoBERT. The training dataset was obtained through the EDA_Synonym_IndoBERT augmentation technique on the UKARA dataset, while the validation and testing datasets consisted of original, non-augmented responses. Model optimization was achieved through the integration of Global Max Pooling to enhance feature representation and class weighting to mitigate class imbalance. Experimental results show that the proposed model achieved an accuracy of 93.49% on the validation set and 78.00% on the independent test set. The performance gap between validation and testing results indicates that, although semantic augmentation increases the diversity of training data, model generalization to previously unseen data remains a challenging issue. Furthermore, the implementation of class weighting improved the model's ability to recognize minority-class instances, achieving a recall score of 92%. These findings demonstrate that architectural optimization and training strategies play a crucial role in improving the performance of Automatic Essay Scoring systems for the Indonesian language

Development of a Project-Based Learning-Based Digital Entrepreneurship Module for JTIK Students

Mon, 30 Mar 2026 00:00:00 +0000

This study aimed to develop a Project-Based Learning (PjBL)-based digital entrepreneurship module for students of the Informatics and Computer Engineering Department and to determine the validity and practicality of the developed module. This research employed the Research and Development (R&D) method using the Four-D (4D) development model consisting of the define, design, develop, and disseminate stages. Data were collected through observation, interviews, and questionnaires involving material experts, media experts, and students of the Informatics and Computer Engineering Department, Universitas Negeri Makassar. The results showed that the developed module obtained a material validity score of 98.89% and a media validity score of 98.79%, both categorized as very valid. Furthermore, the practicality test results showed scores of 84.12% in the small-group trial and 86.84% in the large-group trial, which were categorized as very practical. The module was developed using Canva and presented in an interactive flipbook integrated with Quizizz to support more engaging and interactive project-based entrepreneurship learning. Therefore, the developed digital entrepreneurship module is feasible to be used as a learning material in technology-based entrepreneurship learning.

Progressive Information, Security, Computer, and Embedded System

Optimization of a FastText-Based BiLSTM Model with IndoBERT Semantic Data Augmentation for Indonesian Text Classification

Development of a Project-Based Learning-Based Digital Entrepreneurship Module for JTIK Students