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PREDICTION OF THE POOR RATE K-MEANS AND GENERALIZED REGRESSION NEURAL NETWORK ALGORITHMS (CASE STUDY: NORTH SUMATRA PROVINCE)

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 1 (2023) · pp. 0467–0474

DOI
10.30598/barekengvol17iss1pp0467-0474
ISSN
2615-3017, 1978-7227
Published
2023-04-20
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Poverty reduction is a crucial issue and the primary The North Sumatra Provincial government's main concern is lowering the poverty rate, which is a crucial issue. The Province of North Sumatra in Indonesia, one of many nations affected by the Covid-19 pandemic, is particularly troubled economically. In this study, poverty levels were mapped using the K-Means algorithm, and GRNN was then utilized for modeling and prediction. The data source used is time series data from 2010 to 2020 from the Central Statistics Agency (BPS), which includes variables X covering population, health, education, unemployment, and asset ownership and variable Y representing poverty level. The goal of this study is to choose the best model for estimating poverty levels in North Sumatra Province. The districts and cities of Deli Serdang and Medan have the greatest rates of poverty, according to the K-means algorithm's mapping of poverty levels. Additionally, the results of the predicting produced MSE values of 0.004659 and RMSE values of 0.00002108. The value of the smoothness parameter is 0.01.

Collaboration network of the authors

28 authors · 93 co-authorship links · 2 across different organisations

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  • State University of Medan
  • IPB University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Marlina Setia SinagaAffiliation unknown4
Faridawaty MarpaungAffiliation unknown4
Said Iskandar Al IdrusAffiliation unknown3
Asiah AsiahAffiliation unknown3
Yulita Molliq RangkutiAffiliation unknown1
Didi FebrianAffiliation unknown1
Said IskandarAffiliation unknown1
Sudianto ManullangAffiliation unknown1
Fatizanolo BuuloloAffiliation unknown1
Sri DewiAffiliation unknown1
Eri WidyastutiAffiliation unknown1
Mulyono MulyonoAffiliation unknown1
K M A FauziAffiliation unknown1
Eni YuniastutiAffiliation unknown1
Suvriadi PanggabeanAffiliation unknown1
Hermawan SyahputraState University of Medan1
Agus JunaidiState University of Medan1
M. YaniIPB University1
Fanny RamadhaniAffiliation unknown1
GiaColin Alfaro SamuelAffiliation unknown1
Zakiy Maulana PulunganAffiliation unknown1
Muhammad NasirAffiliation unknown1
Amrizal J. PrangAffiliation unknown1
Yeni SriwahyuniAffiliation 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">PREDICTION OF THE POOR RATE K-MEANS AND GENERALIZED REGRESSION NEURAL NETWORK ALGORITHMS (CASE STUDY: NORTH SUMATRA PROVINCE)</dc:title>
	<dc:creator>Suryani, Nita</dc:creator>
	<dc:creator>Arnita, Arnita</dc:creator>
	<dc:creator>Nabila, Rinjani Cyra</dc:creator>
	<dc:creator>Fitria, Amanda</dc:creator>
	<dc:description xml:lang="en-US">Poverty reduction is a crucial issue and the primary The North Sumatra Provincial government's main concern is lowering the poverty rate, which is a crucial issue. The Province of North Sumatra in Indonesia, one of many nations affected by the Covid-19 pandemic, is particularly troubled economically. In this study, poverty levels were mapped using the K-Means algorithm, and GRNN was then utilized for modeling and prediction. The data source used is time series data from 2010 to 2020 from the Central Statistics Agency (BPS), which includes variables X covering population, health, education, unemployment, and asset ownership and variable Y representing poverty level. The goal of this study is to choose the best model for estimating poverty levels in North Sumatra Province. The districts and cities of Deli Serdang and Medan have the greatest rates of poverty, according to the K-means algorithm's mapping of poverty levels. Additionally, the results of the predicting produced MSE values of 0.004659 and RMSE values of 0.00002108. The value of the smoothness parameter is 0.01.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-04-20</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/7512</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss1pp0467-0474</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0467-0474</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss1year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7512/5564</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Nita Suryani, Arnita Arnita, Rinjani Cyra Nabila, Amanda Fitria</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>