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All Journal Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Journal of Climate Change Society
Nuryadi, Ari
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Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Physics Social Sciences

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Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods Riza, Ozzy Secio; Nuryadi, Ari
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 2 (2023): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v14i2.16618

Abstract

This Research aims to review the machine learning methods used for classifying or predicting rainfall, using various features from existing data. The study used a bibliometric approach to search for metadata related to rainfall classification and prediction studies using machine learning keywords in Scopus journals. There found 94 metadata articles stored in a Comma Separated Values (CSV) file. The data in this article used published articles from 2014 to 2023 with relevant topics. The study provides information on the latest machine learning methods used for classifying or predicting rainfall. The findings of the study include an increase in published articles by 221.43% from 2018 to 2022. The article titled "An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives" by Cramer S., Kampouridis M, Freitas A.A, and Alexandridis A.K received the highest citation count of 129. The study also classified 15 keywords into 3 clusters, with common and fewer keywords. India emerged as the country with the most publications on classifying or predictiong rainfall, and the subject areas of computer science and engineering dominated the distribution of articles. Developing the use of deep learning methods and adding feature extraction algorithms in selecting features used to model data can improve the efficiency and accuracy of the rainfall classification - prediction process. The development of research data using radar images with the type of image processing research can also be maximised for research related to classification - prediction of rainfall using machine learning methods.
Systematic Literature Review of Ocean Wave Renewable Energy Nuryadi, Ari; Yendri Sudiar, Nofi; Hamdi
Journal of Climate Change Society Vol. 1 No. 2 (2023)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jccs/Vol1-iss2/16

Abstract

Renewable energy is a concern to overcome the limitations of fossil energy, one type of renewable energy is ocean wave energy. There are various research topics related to the development of ocean wave energy potential carried out by countries in the world, for this reason the authors conducted a review related to journal papers that discuss ocean wave renewable energy. The method for this study used a Systematic Literature Review (SLR) on journal papers published from 2008-2023, a total of 44 journal papers. The three research questions contained in this study so as to obtain research results that the most countries that carry out research related to ocean wave renewable energy are China. There are a total of nine topics discussed related to ocean wave renewable energy with the most frequently discussed topic trends related to the utilization and modeling of ocean wave renewable energy.
A Systematic Literature Review: Heat Flux and Its Relation to Sea Surface Temperature Variability Samiaji, Budi Isman; Nuryadi, Ari
Journal of Climate Change Society Vol. 2 No. 2 (2024)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jccs/Vol2-iss2/37

Abstract

Heat flow patterns or heat flux play an important role in sea surface temperature (SST) variability and this is very important for understanding the dynamics of climate and marine ecosystems. Heat flow between the atmosphere and ocean can influence ocean circulation patterns, climate variability, and phenomena such as marine heat waves. The aim of this paper is to find out how heat flow patterns or heat flux influence sea surface temperature variability, marine ecology and extreme weather. Using a literature review methodology. It is hoped that from reviewing journals we can find out how heat flows influence sea surface temperature variability and marine ecology and extreme weather. Heat flow between the atmosphere and the ocean has a significant influence on sea surface temperature variability, especially in regions such as the Indian Ocean. Marine heat waves and changes in ocean heat content are clear examples of how heat flows can significantly influence sea surface temperatures. In addition, the influence of long-term heat flow patterns can influence the distribution of species in the sea
Bibliometrik Systematic Literature Review: Heat Flux and Its Relation to Sea Surface Temperature Variability Nuryadi, Ari; Wahyuni, Siltia
Journal of Climate Change Society Vol. 2 No. 2 (2024)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jccs/Vol2-iss2/39

Abstract

Research on atmospheric properties and structure and air quality has become a topic of increasing interest due to the increasing impacts of climate change and environmental degradation. This study aims to analyze publication and citation trends related to atmospheric topics from 2014 to 2024, with a focus on the distribution of research topics, country contributions, and journal sources. Bibliometric analysis was conducted using the Scopus database with the Publish or Perish application, to identify publication patterns, productivity growth, and a significant decrease in the number of citations after 2020. The results show that China and the United States are the main contributors to the number of atmospheric publications, with topics dominated by air pollution emissions and changes in atmospheric chemical composition. However, since 2020, there has been a sharp decline in productivity and citations that coincides with changes in global scientific policies and priorities. These findings provide important insights into the dynamics of atmospheric research, and suggest the need for new strategies to maintain the relevance and sustainability of research in this field. This study is expected to contribute to steering atmospheric research in a direction that is more responsive to global environmental challenges.
Co-Authors Hamdi Riza, Ozzy Secio Samiaji, Budi Isman Wahyuni, Siltia Yendri Sudiar, Nofi