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All Journal Cakrawala Pendidikan Socia : Jurnal Ilmu-Ilmu Sosial Geo Media: Majalah Ilmiah dan Informasi Kegeografian Journal of Tropical Soils Journal of Governance JURNAL GEOGRAFI Geografi dan Pengajarannya Elementaria: Journal of Educational Research International Journal of Geography, Social, and Multicultural Education (IJGSME) Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian
Bambang Syaeful Hadi
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Earth & Planetary Sciences Education Environmental Science Languange, Linguistic, Communication & Media Physics Social Sciences Other

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ChatGPT in Social Studies Education: A Systematic Literature Review of Critical Thinking and Collaborative Learning in Junior High Schools (2023–2026) Isnaeni Fatmiyati; Bambang Syaeful Hadi
Elementaria: Journal of Educational Research Vol. 4 No. 1 (2026): Perspectives on Educational Innovation
Publisher : Penerbit Hellow Pustaka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61166/elm.v4i1.110

Abstract

This study aims to synthesize and analyze the development of research on the use of ChatGPT in Social Studies education to support the development of critical thinking and collaborative learning skills among junior high school students during the 2023–2026 period. This study employed a Systematic Literature Review approach following the PRISMA 2020 guidelines. Literature searches were conducted across Scopus, ERIC, Dimensions, and Google Scholar using keywords related to ChatGPT, Social Studies education, critical thinking, collaborative learning, and junior high school contexts. The inclusion criteria comprised peer-reviewed journal articles published between 2023 and 2026, written in English or Indonesian, focused on Social Studies education, and available in full text. The selection process resulted in eight eligible articles for analysis. The findings indicate that publications on the use of ChatGPT in education have increased substantially since 2023; however, studies specifically addressing Social Studies education at the junior high school level remain limited. The thematic analysis identified four major benefits of ChatGPT, including enhanced critical thinking skills, support for personalized learning, strengthened collaborative learning, and improved access to information. Nevertheless, several challenges were also identified, including academic integrity concerns, the quality of AI-generated responses, teachers’ readiness, and students’ digital literacy. This study contributes to the growing body of literature on the integration of generative artificial intelligence in Social Studies education by identifying existing research gaps and future research directions. Further studies are recommended to develop AI-based instructional models for Social Studies education and empirically examine their effectiveness in junior high school settings.
A Regime-Aware Deep Learning for Long-Term Hydrometeorological Disaster Forecasting (2008–2029): A PELT-LSTM Framework Applied to Indonesia Yonatan yolius anggara; Rosyid Shidiq Hidayatulloh; Nurul Khotimah; Bambang Syaeful Hadi; Suhadi Purwantara
Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian Vol. 23 No. 1 (2026): Volume 23 No 1, June 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jg.v23i1.49495

Abstract

Indonesia's escalating hydrometeorological disaster frequency demands robust predictive frameworks capable of capturing non-stationary climate dynamics. This study aimed to analyze statistical correlations among disaster types and generate long-term flood frequency projections using advanced computational methods applied to national disaster data. Pearson correlation analysis was first conducted to quantify inter-disaster relationships, revealing strong associations between extreme weather, floods, and landslides (r = 0.79–0.86), alongside inverse relationships with drought. The Pruned Exact Linear Time (PELT) algorithm subsequently identified three significant regime shifts in 2012, 2017, and 2022, confirming the progressive non-stationarity of Indonesia's disaster patterns. A Long Short-Term Memory (LSTM) deep learning model was then trained on these regime-structured data to generate predictive forecasts. The model achieved high directional accuracy, successfully capturing the 2025 peak and 2026 decline, with an RMSE of 816.67 and MAPE of 43.77%. Projections for 2027–2029 estimate flood events reaching 2,278, 2,542, and 2,021 incidents respectively, indicating a sustained high-frequency disaster regime that necessitates urgent adaptive infrastructure and evidence-based climate resilience planning.
Co-Authors Abdullah, Nurul Hakimah Che Agus Sudarsono Akhmad, Afandi Arif Ashari Brotowiranti, Rayung Wulan Dina Pramesti Regita Dyah Respati Suryo Sumunar Edi Widodo, Edi Ervin, Muhamad Fathoni, Muhammad Rizal Fitriana, Kamlia Nur Galuh Citraningrum Hastuti Hayu Ratnaningtyas IFFAH NURHAYATI, SRI HARTINI, ANANG PRIYANTO, Ismiatus, Wilda Isnaeni Fatmiyati Janu Eko Herwanto Kurniawati, Risa Amalia Kurniawati Martiana, Aris Melania Swetika Rini Nurhadi Nurhadi Nursida Arif Olivia, Raisya Arzety Razali, Muhammad Syafiq Reswari, Az-Zahra Ardhana Rosyid Shidiq Hidayatulloh Salma, Greace Nanda Putri Soewadi Mulyowiyono, Soewadi Suhadi Purwantara Surjati, Endang Yonatan yolius anggara Yuangwidi, Kinanthi Purbaning Zanyc, Yuinta Diaz Aprilia Zekeri, Fatihin Nabiha Mohd