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ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 2 (2024) · pp. 0837–0848

DOI
10.30598/barekengvol18iss2pp0837-0848
ISSN
2615-3017, 1978-7227
Published
2024-05-25
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.

Collaboration network of the authors

22 authors · 62 co-authorship links

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  • University of Brawijaya
  • Affiliation unknown
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  • Same organisation
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Show as table
Co-authorOrganisationJoint articles
Dimas Avian MaulanaAffiliation unknown6
Affiati OktaviarinaAffiliation unknown6
A’idah Nur HanifahAffiliation unknown3
Junaidi Budi PrihantoAffiliation unknown3
Mohammad Dian PurnamaAffiliation unknown3
Asri MaharaniAffiliation unknown3
Mutia Eva MustafidahAffiliation unknown3
Riskyana Dewi I PuspitasariAffiliation unknown3
Hernando OmbaoAffiliation unknown3
Framitha Septian SubiantoroAffiliation unknown2
Henny PramoedyoUniversity of Brawijaya1
Novi Nur AiniAffiliation unknown1
Kusman SadikAffiliation unknown1
Indahwati IndahwatiAffiliation unknown1
Atiek IrianyUniversity of Brawijaya1
Wigbertus NgabuAffiliation unknown1
A'yunin Sofro SofroAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH</dc:title>
	<dc:creator>Sofro, A'yunin</dc:creator>
	<dc:creator>Riani, Rosalina Agista</dc:creator>
	<dc:creator>Khikmah, Khusnia Nurul</dc:creator>
	<dc:creator>Romadhonia, Riska Wahyu</dc:creator>
	<dc:creator>Ariyanto, Danang</dc:creator>
	<dc:description xml:lang="en-US">Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-05-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/11124</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss2pp0837-0848</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application; 0837-0848</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss2year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/11124/7822</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 A'yunin Sofro, Rosalina Agista Riani, Khusnia Nurul Khikmah, Riska Wahyu Romadhonia, Danang Ariyanto</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>