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Transformasi Kota Cerdas dalam Mitigasi Banjir: Pemodelan Curah Hujan DKI Jakarta dengan Pendekatan Spatial Vector Autoregressive (SpVAR) dan Pemetaan Bobot Queen Contiguity

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 10 No. 6 (2023) · pp. 1285–1294

DOI
10.25126/jtiik.2023107537
ISSN
2528-6579, 2355-7699
Published
2023-12-30
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
id
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Perubahan iklim dan cuaca ekstrem menjadi tantangan global, termasuk di Indonesia, dengan peningkatan banjir di DKI Jakarta. Penanggulangan membutuhkan peramalan curah hujan yang akurat. Model VAR digunakan untuk memahami hubungan variabel cuaca. Namun, data deret waktu sering memiliki dimensi spasial. Oleh karena itu, dikembangkan model Spatial Vector Autoregressive (SpVAR) yang mempertimbangkan dimensi spasial dan waktu. Pembobot queen contiguity digunakan untuk representasi yang lebih akurat. Penelitian ini memanfaatkan data BPS DKI Jakarta dari Januari 2017 hingga Desember 2021. Hasilnya menunjukkan pengaruh spasial dalam model SpVAR (1,3) dengan bobot queen contiguity. Curah hujan, suhu, dan kelembaban udara saling mempengaruhi di wilayah diprediksi dan lainnya. Model ini penting dalam strategi mitigasi banjir dan kebijakan kota cerdas untuk mengurangi risiko banjir di DKI Jakarta. Abstract Climate change and extreme weather pose global challenges, including in Indonesia, leading to increased floods in DKI Jakarta. Addressing this requires accurate rainfall forecasts. The VAR model is used to understand the relationships between weather variables. However, time series data often have spatial dimensions. Therefore, a Spatial Vector Autoregressive (SpVAR) model has been developed considering both spatial and temporal dimensions. Queen contiguity weighting is used for more accurate representation. This study utilizes BPS DKI Jakarta data from January 2017 to December 2021. The results show spatial influence in the SpVAR (1,3) model with queen contiguity weighting. Rainfall, temperature, and humidity mutually influence predicted and other areas. This model is crucial for flood mitigation strategies and smart city policies to reduce flood risks in DKI Jakarta.

Collaboration network of the authors

37 authors · 98 co-authorship links · 9 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • Universitas Negeri Surabaya
  • Universitas Papua
  • University of Bengkulu
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ani Budi AstutiUniversity of Brawijaya2
Wigbertus NgabuUniversity of Brawijaya2
Suci AstutikUniversity of Brawijaya2
Achmad EfendiUniversity of Brawijaya2
Atiek IrianyUniversity of Brawijaya2
Rahma FitrianiUniversity of Brawijaya2
Dwi Ayu LusiaUniversity of Brawijaya1
Karen SematheaAffiliation unknown1
Achmad EfendiAffiliation unknown1
Karen SematheaAffiliation unknown1
Ni Wayan Surya WardhaniUniversity of Brawijaya1
Nabila Ayunda SoviaAffiliation unknown1
Danang AriyantoUniversitas Negeri Surabaya1
Novi Nur AiniUniversity of Brawijaya1
Dahlia Gladiola Rurina MenufanduUniversitas Papua1
Fachri FaisalUniversity of Bengkulu1
Sativandi RizaUniversity of Brawijaya1
Suci Astutik urul Atiqah RomliAffiliation unknown1
Agus YarcanaUniversity of Brawijaya1
Elok PratiwiUniversity of Brawijaya1
Fahimah FauwziyahUniversity of Brawijaya1
Adji FernandesAffiliation unknown1
SolimunAffiliation unknown1
Muhammad NurdinAffiliation unknown1
Muhammad UllahAffiliation unknown1
Andi KurniawanAffiliation unknown1
Moch. Dawam MaghfoerAffiliation unknown1
Sisca FajrianiAffiliation unknown1
Rahma FitrianiAffiliation unknown1
Vita Dewi IslamiAffiliation unknown1
Sudarto SudartoAffiliation unknown1
Sri AndariniAffiliation unknown1
Muhammad AnismuslimAffiliation unknown1
Inayati NgoroAffiliation 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://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Transformasi Kota Cerdas dalam Mitigasi Banjir: Pemodelan Curah Hujan DKI Jakarta dengan Pendekatan Spatial Vector Autoregressive (SpVAR) dan Pemetaan Bobot Queen Contiguity</dc:title>
	<dc:creator xml:lang="id">Melanwati, Rinda Lolita</dc:creator>
	<dc:creator xml:lang="id">Sumarminingsih, Eni</dc:creator>
	<dc:creator xml:lang="id">Pramoedyo, Henny</dc:creator>
	<dc:description xml:lang="id">Perubahan iklim dan cuaca ekstrem menjadi tantangan global, termasuk di Indonesia, dengan peningkatan banjir di DKI Jakarta. Penanggulangan membutuhkan peramalan curah hujan yang akurat. Model VAR digunakan untuk memahami hubungan variabel cuaca. Namun, data deret waktu sering memiliki dimensi spasial. Oleh karena itu, dikembangkan model Spatial Vector Autoregressive (SpVAR) yang mempertimbangkan dimensi spasial dan waktu. Pembobot queen contiguity digunakan untuk representasi yang lebih akurat. Penelitian ini memanfaatkan data BPS DKI Jakarta dari Januari 2017 hingga Desember 2021. Hasilnya menunjukkan pengaruh spasial dalam model SpVAR (1,3) dengan bobot queen contiguity. Curah hujan, suhu, dan kelembaban udara saling mempengaruhi di wilayah diprediksi dan lainnya. Model ini penting dalam strategi mitigasi banjir dan kebijakan kota cerdas untuk mengurangi risiko banjir di DKI Jakarta.
 
Abstract
Climate change and extreme weather pose global challenges, including in Indonesia, leading to increased floods in DKI Jakarta. Addressing this requires accurate rainfall forecasts. The VAR model is used to understand the relationships between weather variables. However, time series data often have spatial dimensions. Therefore, a Spatial Vector Autoregressive (SpVAR) model has been developed considering both spatial and temporal dimensions. Queen contiguity weighting is used for more accurate representation. This study utilizes BPS DKI Jakarta data from January 2017 to December 2021. The results show spatial influence in the SpVAR (1,3) model with queen contiguity weighting. Rainfall, temperature, and humidity mutually influence predicted and other areas. This model is crucial for flood mitigation strategies and smart city policies to reduce flood risks in DKI Jakarta.</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2023-12-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/7537</dc:identifier>
	<dc:identifier>10.25126/jtiik.2023107537</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 10 No 6: Desember 2023; 1285-1294</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 10 No. 6: Desember 2023; 1285-1294</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.106</dc:source>
	<dc:language>id</dc:language>
	<dc:relation>https://jtiik.ub.ac.id/index.php/jtiik/article/view/7537/1204</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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