Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Electrical Engineering and Informatics
Dendi Permana
Universitas Pelita Bangsa
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
An Explainable AI Framework for Bias Detection in Indonesian Sentiment Analysis Arif Tri Widiyatmoko; Arif Siswandi; Amali Amali; Intan Virginia Aulia; Dendi Permana
Journal of Electrical Engineering and Informatics Vol. 4 No. 1 (2026): Journal of Electrical Engineering and Informatics
Publisher : Fakultas Teknik Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jeeni.v4i1.14484

Abstract

The development of deep learning models in sentiment analysis has significantly improved the accuracy of text classification. However, these models are generally black-box, making them difficult to interpret and potentially contain undetectable biases. This study aims to detect and evaluate bias in sentiment analysis models using SHAP-based Explainable Artificial Intelligence (XAI) approach. The model used is IndoBERT which is trained on the Indonesian sentiment dataset. The research stages include data preprocessing, model training, interpretability analysis using SHAP, and fairness evaluation using disparate impact metrics. The results showed that the model achieved an accuracy of 88%, but SHAP analysis revealed the dominance of certain features in decision-making. In addition, fairness evaluation shows that there is an inequality in the distribution of predictions between groups. These findings show that high accuracy does not guarantee model fairness, so Explainable AI integration is important in the development of transparent and accountable NLP systems. 
Co-Authors Amali Amali Arif Siswandi Arif Tri Widiyatmoko Intan Virginia Aulia