Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Telematika Journal of Applied Data Sciences
Taqwa Hariguna
Magister of Computer Science, Amikom Purwokerto University, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Fairness Auditing and Bias Mitigation in Aspect-Based Sentiment Models for Indonesian Public Services Muhammad Shihab Fathurrahman Jondien; Taqwa Hariguna; Dhanar Intan Surya Saputra
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3269

Abstract

This study presents a comprehensive fairness audit and bias mitigation framework for Indonesian sentiment analysis using the SmSA IndoNLU dataset and the IndoBERT language model. The research investigates demographic and linguistic fairness by evaluating model performance across gender and regional groups and introduces an aspect-based extension to assess semantic fairness using an ABSA-style input formulation. Fairness metrics such as ΔF1, Demographic Parity Difference (DPD), and Equality of Opportunity were employed to quantify disparities in model behavior. The baseline IndoBERT model achieved strong overall accuracy (0.942) and macro-F1 (0.927) but exhibited significant regional bias, particularly toward Eastern and Sumatran dialects. A re-weighting strategy effectively reduced the regional F1 disparity by 59 percent with minimal accuracy loss, demonstrating the viability of loss-based fairness mitigation. The ABSA-style IndoBERT further improved fairness consistency across dialectal and aspect categories, achieving a macro-F1 of 0.930. Despite these improvements, aspect-level imbalances persisted, indicating that fairness challenges extend beyond demographic representation to semantic coverage. This work contributes an empirical and methodological foundation for ethical NLP evaluation in Bahasa Indonesia, emphasizing fairness auditing, bias mitigation, and responsible deployment of language models in low-resource and linguistically diverse settings.
Enhancing the Robustness of Adaptive Class Activation Mapping (AD-CAM) Against Noisy Facial Expression Data Using Preprocessing and Adaptive Normalization Dwi Sugianto; Taqwa Hariguna; Fandy Setyo Utomo
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1005

Abstract

In real-world computer vision applications, visual data is often corrupted by noise, reducing both the accuracy and interpretability of deep learning models. This study proposes an enhanced AD-CAM framework that integrates noise-aware preprocessing and adaptive normalization to improve robustness in both prediction and visual explanation. Experiments were conducted on the FER2013 facial expression dataset augmented with Gaussian, salt-and-pepper, and speckle noise. Using ResNet-50 as the backbone, the proposed method demonstrated significant gains across multiple evaluation metrics, including Robust Accuracy (RA), Drop Coherence (DC), Area Under Robustness Curve (AURC), and Signal-to-Noise Ratio (SNR). Compared to the baseline, the model achieved over 10% accuracy improvement and up to 0.16 DC reduction under noise. Qualitative visualizations showed that the improved model consistently highlighted semantically relevant facial regions, maintaining interpretability even under severe input degradation. These results support the adoption of noise-aware interpretability frameworks for more reliable and trustworthy deployment in real-world vision systems.
Co-Authors Dhanar Intan Surya Saputra Dwi Sugianto Fandy Setyo Utomo Muhammad Shihab Fathurrahman Jondien