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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) · hlm. 0467–0474

DOI
10.30598/barekengvol17iss1pp0467-0474
ISSN
2615-3017, 1978-7227
Terbit
2023-04-20
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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  • State University of Medan
  • IPB University
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Rekan penulisOrganisasiArtikel bersama
Marlina Setia SinagaAfiliasi tidak diketahui4
Faridawaty MarpaungAfiliasi tidak diketahui4
Said Iskandar Al IdrusAfiliasi tidak diketahui3
Asiah AsiahAfiliasi tidak diketahui3
Yulita Molliq RangkutiAfiliasi tidak diketahui1
Didi FebrianAfiliasi tidak diketahui1
Said IskandarAfiliasi tidak diketahui1
Sudianto ManullangAfiliasi tidak diketahui1
Fatizanolo BuuloloAfiliasi tidak diketahui1
Sri DewiAfiliasi tidak diketahui1
Eri WidyastutiAfiliasi tidak diketahui1
Mulyono MulyonoAfiliasi tidak diketahui1
K M A FauziAfiliasi tidak diketahui1
Eni YuniastutiAfiliasi tidak diketahui1
Suvriadi PanggabeanAfiliasi tidak diketahui1
Hermawan SyahputraState University of Medan1
Agus JunaidiState University of Medan1
M. YaniIPB University1
Fanny RamadhaniAfiliasi tidak diketahui1
GiaColin Alfaro SamuelAfiliasi tidak diketahui1
Zakiy Maulana PulunganAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 5 Nov 2026.
Metadata asli (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>