Nadya Tridrisna Manurung
Universitas Negeri Medan

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The Phenomenon of Political Participation Apathy: Analysis of Voter Turnout Decline in the 2024 Regional Head Elections of Cirebon Regency Nadya Tridrisna Manurung; Sunoto Sunoto
Edueksos: Jurnal Pendidikan Sosial & Ekonomi Vol. 14 No. 02 (2025)
Publisher : Department of Tadris IPS FITK UIN Siber Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/edueksos.v14i02.22180

Abstract

This study analyzes the phenomenon of declining voter participation in the 2024 Cirebon Regency Regional Head Elections (Pilkada). This phenomenon is of concern because political participation is an important indicator of the quality of local democracy. The purpose of this study is to identify the factors causing the decline in participation and to analyze its implications for the democratization process at the regional level. Using a qualitative method with a descriptive approach, this study involved various elements of society, such as religious leaders, village officials, youth, students, and election organizers. Data were collected through field observations and in-depth interviews with key informants, then analyzed thematically to find patterns and meanings behind the phenomenon. The results show that the decline in voter participation is influenced by a combination of structural and cultural factors, including low political awareness, increased population mobility, the policy of regrouping polling stations (TPS), and growing apathy towards local politics. Young voters and urban communities tend to be indifferent due to a lack of political literacy and disappointment with regional head candidates who are considered to bring no change. The regrouping of polling stations, which increases the number of voters per location, also reduces the convenience and effectiveness of voting. These findings imply the need for strategies to increase participation through continuous political education, innovation in voting systems that are adaptive to citizen mobility, and political campaigns based on concrete ideas and programs to restore public trust in the democratic process.Keywords: political participation, regional elections, Cirebon, apathy, local democracy.
The Use of Artificial Intelligence in History Learning: A Systematic Literature Review Agus Rahmat Mahmudi; Nadya Tridrisna Manurung; Nurzengky Ibrahim; Andy Suryadi
Fajar Historia: Jurnal Ilmu Sejarah dan Pendidikan Vol 10 No 2 (2026): Agustus
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/fhs.v10i2.34717

Abstract

This systematic literature review synthesizes empirical and conceptual scholarship on the application of AI-in history education between 2020 and 2026, interrogating research trends, pedagogical affordances, ethical risks, and policy implications. Employing a PRISMA-guided search across SCOPUS, Web of Science, and ERIC and a PICOC-framed analytic scope, the study screened 312 records (after deduplication) and conducted in-depth analysis of 10 peer-reviewed articles using thematic synthesis, bibliometric mapping, and quality appraisal (CASP). Findings indicate a clear disciplinary trajectory from conceptual explorations toward classroom-embedded uses of generative AI and learning analytics, with demonstrated benefits in student engagement, adaptive feedback, dialogic inquiry, and immersive simulation design that can scaffold elements of historical thinking. However, the corpus reveals persistent limitations: small-scale designs, Western-centric datasets, epistemic misalignments with historical heuristics (sourcing, contextualization, evidentiary weighting), algorithmic bias, opacity, and equity gaps that risk historiographical flattening and the reproduction of structural inequalities. The review concludes that AI’s pedagogical value in history depends on three interlocking priorities targeted teacher professional development integrating digital and historiographical literacies, context-sensitive governance ensuring explainability and data justice, and ethical-by-design AI architectures with traceable source attribution to safeguard epistemic rigor and inclusive learning outcomes.