Mapping and visualization the research of climate change adaptation using artificial intelligence in Indonesia: A bibliometric analysis
-
Khaidar Ali
,
Serius Miliyani Dwi Putri
,
Muhammad Addin Rizaldi
,
Agnes Fitria Widiyanto
,
Suratman Suratman
,
R Azizah
Abstract
Climate change is not only contributing to the proliferation of infectious and vector-borne communicable diseases is a major concern, but also escalating the risk of extreme weather among community, in which research on climate change adaptation using advanced technology is necessary. This study aimed to investigate research trend on climate change adaptation in Indonesia concerning on the utilization of novel technology and artificial intelligence. This study employed bibliographic analysis using Scopus article database during 2000-2023. The total sampling technique was used, in which every relevant document within inclusion criteria were included in the study. The analysis was conducted in R Studio, in which network analysis was measured
by VOSviewer. A total of 1,858 articles is identified. The annual of publication growth rate is 17.77%, with the average citation per document is 29. The university situated in Java Island-Indonesia was leading institution for publication. Sustainability and Biodiversitas are the most prominent journals. The scholars with high
publication and citation are Yulianto (13 articles) and Murdiyarso (1,819 citation). Eight clusters have been recorded, with the most prominent term is “climate change”, "adaptation", "flood", "remote sensing", "agriculture", and "vulnerability". This study found the research interest on climate change adaptation is elevating each year in Indonesia. The application of advanced technology, such as artificial intelligence, machine learning, and Internet of Things (IoT) remains relatively unexplored. Therefore, future research on climate change adaptation using advanced technology in Indonesia is needed to provide comprehensive knowledge, enhance predictive capabilities, and provide innovative solution to manage the effect of climate change.
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2. Hötte K, Jee SJ. Knowledge for a warmer world: A patent analysis of climate change adaptation technologies. Technol Forecast Soc Change [Internet]. 2022;183(December 2021):121879. Available from: https://doi.org/10.1016/j.techfore.2022.121879
3. Jain H, Dhupper R, Shrivastava A, Kumar D, Kumari M. AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change. Comput Urban Sci [Internet]. 2023;3(1). Available from: https://doi.org/10.1007/s43762-023-00100-2
4. Jimma BL. Artificial intelligence in healthcare: A bibliometric analysis. Telemat Informatics Reports. 2023;9(June 2022).
5. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism [Internet]. 2017;69:S36–40. Available from: http://dx.doi.org/10.1016/j.metabol.2017.01.011
6. Aprilia AHZC. Artificial Intelligence [Internet]. 2024 [cited 2024 Sep 2]. Available from: https://www.djkn.kemenkeu.go.id/kpknl-bandaaceh/baca-artikel/16443/Artificial-Intelligence
7. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med [Internet]. 2019;25(1):30–6. Available from: https://doi.org/10.1038/s41591-018-0307-0
8. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA [Internet]. 2013 Apr;309(13):1351–2. Available from: https://doi.org/10.1001/jama.2013.393
9. Dreyer KJ, Geis JR. When Machines Think: Radiology’s Next Frontier. Radiology [Internet]. 2017 Nov 20;285(3):713–8. Available from: https://doi.org/10.1148/radiol.2017171183
10. Kruse CS, Goswamy R, Raval Y, Marawi S. Challenges and Opportunities of Big Data in Health Care: A Systematic Review. JMIR Med informatics. 2016 Nov;4(4):e38.
11. Sandalow D, McCormick C, Kucukelbir A, Friedmann J, Zhiyuan Fan C, Halff A, et al. AI and climate change roadmap CCAI. 2023;(December).
12. Kreps GL, Neuhauser L. Artificial intelligence and immediacy: Designing health communication to personally engage consumers and providers. Patient Educ Couns [Internet]. 2013;92(2):205–10. Available from: https://www.sciencedirect.com/science/article/pii/S0738399113001729
13. Niu B, Hong S, Yuan J, Peng S, Wang Z, Zhang X. Global trends in sediment-related research in earth science during 1992–2011: a bibliometric analysis. Scientometrics [Internet]. 2014;98(1):511–29. Available from: https://doi.org/10.1007/s11192-013-1065-x
14. Stryker C, Kavkoglu E. What is AI? [Internet]. 2024 [cited 2024 Sep 12]. Available from: https://www.ibm.com/topics/artificial-intelligence
15. Rajpurohit D singh, Seal R. Legal Definition of Artificial Intelligence. Supremoamicus. 2019;10.
16. Leal Filho W, Wall T, Rui Mucova SA, Nagy GJ, Balogun AL, Luetz JM, et al. Deploying artificial intelligence for climate change adaptation. Technol Forecast Soc Change [Internet]. 2022;180(April). Available from: https://doi.org/10.1016/j.techfore.2022.121662
17. Cowls J, Tsamados A, Taddeo M, Floridi L. The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI Soc [Internet]. 2023;38(1):283–307. Available from: https://doi.org/10.1007/s00146-021-01294-x
18. Karch JD, Brandmaier AM, Voelkle MC. Gaussian Process Panel Modeling—Machine Learning Inspired Analysis of Longitudinal Panel Data. Front Psychol [Internet]. 2020;11(March):1–20. Available from: https://doi.org/10.3389/fpsyg.2020.00351
19. Chen L, Chen Z, Zhang Y, Liu Y, Osman AI, Farghali M, et al. Artificial intelligence-based solutions for climate change: a review [Internet]. Vol. 21, Environmental Chemistry Letters. Springer International Publishing; 2023. 2525–2557 p. Available from: https://doi.org/10.1007/s10311-023-01617-y
20. Mayfield H, Smith C, Gallagher M, Hockings M. Use of freely available datasets and machine learning methods in predicting deforestation. Environ Model Softw. 2017;87:17–28.
21. Torres VAMF, Jaimes BRA, Ribeiro ES, Braga MT, Shiguemori EH, Velho HFC, et al. Combined weightless neural network FPGA architecture for deforestation surveillance and visual navigation of UAVs. Eng Appl Artif Intell [Internet]. 2020;87(April 2019):103227. Available from: https://doi.org/10.1016/j.engappai.2019.08.021
22. Gautam K, Sharma P, Dwivedi S, Singh A, Gaur VK, Varjani S, et al. A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil. Environ Res [Internet]. 2023;225(August 2022):115592. Available from: https://doi.org/10.1016/j.envres.2023.115592
23. Liu T, Chen L, Yang M, Sandanayake M, Miao P, Shi Y, et al. Sustainability Considerations of Green Buildings: A Detailed Overview on Current Advancements and Future Considerations. Sustain. 2022;14(21):1–23.
24. Shailesh Kulkarni, Ramswaroop Reddy Yellu, Nidhi Chauhan BMPKMCB. AI-Driven Energy Management Systems for Smart Buildings. Power Syst Technol. 2024;48(1):322–37.
25. Rejeb A, Simske S, Rejeb K, Treiblmaier H, Zailani S. Internet of Things research in supply chain management and logistics: A bibliometric analysis. Internet of Things (Netherlands). 2020;12.
26. Wahyuni H, Vanany I, Ciptomulyono U. 32.the supply chain: Review and bibliometric analysis. J Ind Eng Manag [Internet]. 2019;12(2):373–91. Available from: https://api.elsevier.com/content/abstract/scopus_id/85070109859
27. Apriliyanti ID, Alon I. Bibliometric analysis of absorptive capacity. Int Bus Rev [Internet]. 2017;26(5):896–907. Available from: http://dx.doi.org/10.1016/j.ibusrev.2017.02.007
28. Wu X, Shen YS, Cui S. Global Trends in Green Space and Senior Mental Health Studies: Bibliometric Review. Int J Environ Res Public Health [Internet]. 2023;20(2). Available from: https://doi.org/10.3390/ijerph20021316
29. Madjido M. Pemetaan topik publikasi sistem informasi kesehatan di indonesia : analisis bibliometrik 65 masry madjido. 2019;4(1):65–8. Available from: https://doi.org/10.22146/jisph.44122
30. Foncubierta-Rodríguez A, Müller H, Depeursinge A. Retrieval of high-dimensional visual data: current state, trends and challenges ahead. Multimed Tools Appl [Internet]. 2014;69(2):539–67. Available from: https://doi.org/10.1007/s11042-012-1327-2
31. Maflahi N, Thelwall M. When are readership counts as useful as citation counts? Scopus versus Mendeley for LIS journals. J Assoc Inf Sci Technol [Internet]. 2016 Jan 1;67(1):191–9. Available from: https://doi.org/10.1002/asi.23369
32. Ahmi A. OpenRefine: An approachable tool for cleaning and harmonizing bibliographical data. AIP Conf Proc [Internet]. 2023 Sep 12;2827(1):30006. Available from: https://doi.org/10.1063/5.0164724
33. Ge Z, Liu J, Zhong C. Uncovering the mineral constraints on energy transition under climate change targets : A bibliometric review. Energy Strateg Rev [Internet]. 2024;55(July):101520. Available from: https://doi.org/10.1016/j.esr.2024.101520
34. Baraj B, Mishra M, Sudarsan D, Silva RM da, Santos CAG. Climate change and resilience, adaptation, and sustainability of agriculture in India: A bibliometric review. Heliyon [Internet]. 2024;10(8). Available from: https://doi.org/10.1016/j.heliyon.2024.e29586
35. Kurniawan TA, Meidiana C, Goh HH, Zhang D, Othman MHD, Aziz F, et al. Unlocking synergies between waste management and climate change mitigation to accelerate decarbonization through circular-economy digitalization in Indonesia. Sustain Prod Consum [Internet]. 2024;46(January):522–42. Available from: https://doi.org/10.1016/j.spc.2024.03.011
36. Baidya A, Saha AK. Exploring the research trends in climate change and sustainable development: A bibliometric study. Clean Eng Technol [Internet]. 2024;18(August 2023):100720. Available from: https://doi.org/10.1016/j.clet.2023.100720
37. Waseem H Bin, Mirza MNEE, Rana IA, Waheed A. Adaptation planning for climate change: An application of the advanced bibliometric analytical framework. Nat Hazards Res [Internet]. 2024;4(3):459–69. Available from: https://doi.org/10.1016/j.nhres.2023.11.005
38. Saptutyningsih E, Diswandi D, Jaung W. Does social capital matter in climate change adaptation? A lesson from agricultural sector in Yogyakarta, Indonesia. Land use policy [Internet]. 2020;95(August 2019):104189. Available from: https://doi.org/10.1016/j.landusepol.2019.104189
39. Azizah R, Mohamed AFH, Sulistyorini L, Mulia SA, Arfiani ND, Rahmawati A. Analysis of waste management effect on the climate related disease in Larangan Village, Sidoarjo. Env Anal Heal Toxicol [Internet]. 2024 Mar 26;39(1):e2024010-0. Available from: https://doi.org/10.5620/eaht.2024010
40. Rusmili SH, Mohamad Hamzah F, Choy LK, Azizah R, Sulistyorini L, Yudhastuti R, et al. Ground-Level Particulate Matter (PM2.5) Concentration Mapping in the Central and South Zones of Peninsular Malaysia Using a Geostatistical Approach. Vol. 15, Sustainability. 2023.
41. Wardani RA, Azizah R. Management of Solid Medical Waste on One of the Covid19 Referral Hospitals in Surabaya, East Java. J Kesehat Lingkung. 2020;12(1 Special Issue):38–44.
42. du Plessis F, Goedhals-Gerber L, van Eeden J. The impacts of climate change on marine cargo insurance of cold chains: A systematic literature review and bibliometric analysis. Transp Res Interdiscip Perspect [Internet]. 2024;23(January):101018. Available from: https://doi.org/10.1016/j.trip.2024.101018
43. Waltman L, van Eck NJ. A smart local moving algorithm for large-scale modularity-based community detection. Eur Phys J B [Internet]. 2013;86(11):471. Available from: https://doi.org/10.1140/epjb/e2013-40829-0
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| Files | ||
| Issue | Vol 10 No 2 (2025): Spring 2025 | |
| Section | Review Article(s) | |
| Keywords | ||
| Climate change; Adaptation; Artificial intelligence; Technology; Bibliometric | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Ali K, Dwi Putri S, Rizaldi M, Widiyanto A, Suratman S, Azizah R. Mapping and visualization the research of climate change adaptation using artificial intelligence in Indonesia: A bibliometric analysis. JAPH. 2025;10(2):291-310.
