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All Journal Journal of Innovation Information Technology and Application (JINITA) GUYUB: Journal of Community Engagement
Shofwatul Uyun
Universitas Islam Negeri Sunan Kalijaga Yogyakarta
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Demystifying Political Hate Speech Detection: An Explainable Artificial Intelligence Audit of Transformer and Linear Models Aulia Miftah Razak; Shofwatul Uyun; Abdul Rozak
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3205

Abstract

The rise of political hate speech on social media calls for detection systems that are not only accurate but also transparent to avoid moderation bias. Although Transformer models achieve high performance, their “black box” nature creates the risk of a “false sense of security” in content moderation, where high accuracy can mask systemic bias. This study aims to transparently audit the decision-making mechanisms of a lexical-feature-based Support Vector Machine (SVM) model and a contextual-representation-based IndoBERT model using an Explainable AI (XAI) approach via the LIME method. Experimental results show that IndoBERT significantly outperforms SVM with a Macro-F1 score of 90.8% versus 84.0%. However, the XAI audit revealed the presence of data-driven bias in both models toward specific political entities such as “Jokowi,” “Prabowo,” “cebong,” and “kampret,” which often trigger negative labels automatically without a comprehensive contextual review. These findings underscore that transparency audits through XAI serve as a crucial bridge for building a content moderation system that is fair, accountable, and capable of protecting freedom of expression within the digital democratic ecosystem.
Improving Students’ Knowledge of Biomonitoring through Service Learning in Higher Education Institution Eka Sulistiyowati; Dien F. Awaliyah; Shofwatul Uyun
GUYUB: Journal of Community Engagement Vol 6, No 3 (2025): September
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/guyub.v6i3.12431

Abstract

Service learning in biomonitoring is urgent as it links science with community action to tackle river health issues.. This research aims to explore the application of service learning in enhancing students' knowledge and their ability to carry out river health biomonitoring projects. The study involved students in implementing the service learning curriculum through stages of planning, execution, reflection, and assessment. During the planning phase, students participated in developing the module. The results indicated that the biomonitoring module received a quality score of 3.8, with clarity of content and factual accuracy achieving the highest scores (4.0). The service learning program was conducted through the establishment of ECOFOREST groups, training sessions, and the application of action plans within the community. The effectiveness was measured using a one-group pretest-posttest design, which revealed a significant improvement in student understanding (t(22) = 2.45, p < 0.05). These findings confirm that service learning not only enhances student engagement in the community but also contributes to their technical competency development. This study addresses the gap in literature regarding service learning within more practical experiential learning frameworks in higher education.The result implies that there has been an increase of knowledge among the participants.
Co-Authors Abdul Rozak Aulia Miftah Razak Dien F. Awaliyah Eka Sulistiyowati