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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jesya (Jurnal Ekonomi dan Ekonomi Syariah) Jurnal Pengabdian kepada Masyarakat Nusantara INFORMATIKA Dinamika Community: Jurnal Pengabdian Pada Masyarakat Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Teknik Informatika dan Teknologi Informasi
Agus Priyadi
Universitas Sains Dan Teknologi Komputer, Semarang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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AI-Generated Narratives and Infographic Synthesis for Visualizing Climate Temperature Anomalies Azed Yayah Durrotun Nihayah; Agus Priyadi; Irdha Yunianto; Fitro Nur Hakim; Wiwid Wahyudi; Nafeeza Nafeeza
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7060

Abstract

Communicating long-term climate trends to non-specialist audiences remains a persistent challenge, despite the availability of well-validated global temperature datasets. While existing climate visualizations and reports provide accurate information, they often rely on expert-driven interpretations or static representations that limit accessibility and scalability. This study presents a proof-of-concept system that integrates analytical processing, rule-based narrative generation, and infographic synthesis to transform structured climate data into coherent public-facing communication artifacts. The proposed framework uses the NASA GISTEMP v4 dataset, covering annual global temperature anomalies from 1880 to 2024. It applies linear trend estimation and deterministic anomaly highlighting to extract salient temporal patterns. These analytical outputs are then translated into traceable natural language summaries and integrated with visual encodings within a single reproducible pipeline. The results confirm a persistent long-term warming trend, with several recent years exceeding high-anomaly thresholds, and demonstrate that analytical values, narrative descriptions, and visual emphasis can be generated consistently from a shared data source. Rather than introducing new climate indicators or predictive models, this study’s contribution lies in system-level integration: coupling data analysis, narrative synthesis, and visual composition into a unified, communication-oriented workflow. The framework is explicitly positioned as a proof of concept and does not claim causal attribution or empirical validation of user impact. Nonetheless, it demonstrates how transparent automation can reduce reliance on expert mediation while preserving scientific fidelity, supporting scalable climate communication systems.
Co-Authors Ahmad Zainudin Ahmad Zainudin Akhmad Cholif Agung Yuliyanto ALDO KRISDIANANTA Andik Prakasa Hadi Andreas Heri Kurniawan Azed Yayah Durrotun Nihayah Candra Supriadi Danang Danang Dani Sasmoko Daniel Rudjiono Dewi Prehantini Wahyuningtyas Dewi Widyaningsih Dimas Pamungkas Edwin Zusrony Edy Jogatama Purhita Eko Rustanto ENI ENDARYATI Eni Kristina Pujiastuti Firda Vera Salsabila Fitro Nur Hakim Indra Ava Dianta Irda Yunianto Irdha Yunianto Krisna Danu Aji Saputra Kusumajaya, Robby Andika Nafeeza Nafeeza Nining Fitriani Nur Rokhman Pindo Asti Priyadi Priyadi Priyadi Raden Mohamad Herdian Bhakti Ridlo Qoirul Anhari Rizky Ridwan Saputra Sekar Lintang Rachmawati Setiyo Adi Nugroho Setiyo Adi Nugroho Setyo Adi Nugroho Siti Kholifah Siti Sohiroh Siti Zumatun Kumaeroh Theo Hendrawan Tama Vivi Kumalasari Wiwid Wahyudi Wiwid Wahyudi