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Eligibility of village fund direct cash assistance recipients using artificial neural network

IAES International Journal of Artificial Intelligence · Vol. 12 No. 4 (2023) · hlm. 1611–1618

DOI
10.11591/ijai.v12.i4.pp1611-1618
ISSN
2252-8938, 2089-4872
Terbit
2023-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Bantuan Langsung Tunai Dana Desa (BLT-DD), or known as Village Fund Direct Cash Assistance is assistance from the Indonesian government which causes problems and conflicts in the community when the assistance is not on target. The classification algorithm is proven to use in determining BLT-DD recipients. In this study, the radial basis function (RBF) and elman recurrent neural network (ERNN) models compare to classify the eligibility of BLTDD recipients. In the experiment, the optimal performance of the RBF and ERNN compare in determining the eligibility of BLT-DD recipients. Also, it’s compared with the classification algorithm that implements the same data, namely BLT-DD data for Kubu Raya District. The experimental results show the effectiveness of the RBF model in recognizing test data, while the ERNN model is effective in identifying test data. The RBF and ERNN models can achieve the same total accuracy of 98.10%.

Kata kunci

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22 penulis · 79 tautan kepenulisan bersama · 53 lintas organisasi

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  • Tanjungpura University
  • Khairun University
  • UIN Sunan Ampel Surabaya
  • Indonesia University of Education
  • Hasanuddin University
  • National Research and Innovation Agency
  • Politeknik Pertanian Negeri Pangkajene Kepulauan
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  • Organisasi berbeda
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Rekan penulisOrganisasiArtikel bersama
Syamsul BahriTanjungpura University2
Ilhamsyah IlhamsyahTanjungpura University2
Zalikhah KhairunnisaTanjungpura University2
Muhammad IqbalHasanuddin University1
Syamsul BahriTanjungpura University1
Andi SantiPoliteknik Pertanian Negeri Pangkajene Kepulauan1
Nancy KiayState University of Gorontalo1
Hamidin RasuluKhairun University1
Janiah HusenKhairun University1
Angela WulansariKhairun University1
Ikrima M. MustafaKhairun University1
Asep NurhikmatNational Research and Innovation Agency1
Nurjanna AlbaarKhairun University1
Mila FatmawatiKhairun University1
Nabel Ahmed A MansourAljafara University1
Burhan DjamaluddinUIN Sunan Ampel Surabaya1
Nurlailah NurlailahUIN Sunan Ampel Surabaya1
Ahmad ZamzamyUniversity of Pembangunan Nasional Veteran Jawa Timur1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Eligibility of village fund direct cash assistance recipients using artificial neural network</dc:title>
	<dc:creator>Midyanti, Dwi Marisa</dc:creator>
	<dc:creator>Bahri, Syamsul</dc:creator>
	<dc:creator>Suhardi, Suhardi</dc:creator>
	<dc:creator>Midyanti, Hafizhah Insani</dc:creator>
	<dc:subject xml:lang="en-US">Neural Network; Radial Basis Function, Elman Recurrent Neural Network</dc:subject>
	<dc:subject xml:lang="en-US">Classification; Elman recurrent neural network; Neural network; Radial basis function; Village fund direct cash assistance</dc:subject>
	<dc:description xml:lang="en-US">Bantuan Langsung Tunai Dana Desa (BLT-DD), or known as Village Fund Direct Cash Assistance is assistance from the Indonesian government which causes problems and conflicts in the community when the assistance is not on target. The classification algorithm is proven to use in determining BLT-DD recipients. In this study, the radial basis function (RBF) and elman recurrent neural network (ERNN) models compare to classify the eligibility of BLTDD recipients. In the experiment, the optimal performance of the RBF and ERNN compare in determining the eligibility of BLT-DD recipients. Also, it’s compared with the classification algorithm that implements the same data, namely BLT-DD data for Kubu Raya District. The experimental results show the effectiveness of the RBF model in recognizing test data, while the ERNN model is effective in identifying test data. The RBF and ERNN models can achieve the same total accuracy of 98.10%.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/22758</dc:identifier>
	<dc:identifier>10.11591/ijai.v12.i4.pp1611-1618</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 4: December 2023; 1611-1618</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v12.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/22758/13735</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>