Implementation of Data Mining with Naive Bayes Algorithm for Eligibility Classification of Basic Food Aid Recipients

Shino, Yamato; Durachman, Yusuf; Sutisna, Nana

doi:10.34306/ijcitsm.v2i2.114

Akses terbuka Jurnal SINTA 1

Implementation of Data Mining with Naive Bayes Algorithm for Eligibility Classification of Basic Food Aid Recipients

International Journal of Cyber and IT Service Management · Vol. 2 No. 2 (2022) · hlm. 154–162

DOI: 10.34306/ijcitsm.v2i2.114
ISSN: 2808-554X, 2797-1325
Terbit: 2022-09-21
Penerbit: Pandawan Sejahtera Indonesia
Bahasa: en
Lisensi: creativecommons.org/licenses/by/4.0/

Abstrak

One of the primary issues that the government of a nation concentrates on is poverty. The provision of precise and focused data on poverty is a crucial component of the Poverty Reduction Strategy. One technique for classifying data is Naïve Bayes. The aid manager will subsequently use the categorization findings to inform judgments about categorizing and determining who should get basic food assistance. Predictions for those who get basic food assistance fall into two categories: eligible and ineligible. Sample data from the hamlet of XYZ used as the basis for the forecast. In this study, a web-based application is used to construct and assess the Naïve Bayes method. The accuracy for 135 training data, 40 test data, and seven characteristics employed generates 86 percent accuracy, 85 percent recall, and 88 percent precision according to the assessment findings using the confusion matrix.

Kata kunci

Jaringan kolaborasi para penulis

14 penulis · 32 tautan kepenulisan bersama

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

Universitas Raharja
Afiliasi tidak diketahui

Organisasi berbeda
Organisasi sama
Afiliasi tidak diketahui
Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel

Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Hiroshi Kenta	Afiliasi tidak diketahui	2
Adrian Sean Bein	Afiliasi tidak diketahui	1
Eka Purnama Harahap	Afiliasi tidak diketahui	1
Tarisya Ramadhan	Afiliasi tidak diketahui	1
Fitra Putri Oganda	Afiliasi tidak diketahui	1
Lee Kyung Choi	Afiliasi tidak diketahui	1
Kim Beom Rii	Afiliasi tidak diketahui	1
Irsa Rizkita Adianita	Afiliasi tidak diketahui	1
Eka Purnama Harahap	Universitas Raharja	1
Dewi Immaniar	Afiliasi tidak diketahui	1
Alfian Dimas Ahsanul Rizki Ahmad	Afiliasi tidak diketahui	1

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) https://iiast.iaic-publisher.org/ijcitsm/index.php/IJCITSM/oai, pertama di-harvest 6 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">Implementation of Data Mining with Naive Bayes Algorithm for Eligibility Classification of Basic Food Aid Recipients</dc:title>
	<dc:creator xml:lang="en">Shino, Yamato</dc:creator>
	<dc:creator xml:lang="en">Durachman, Yusuf</dc:creator>
	<dc:creator xml:lang="en">Sutisna, Nana</dc:creator>
	<dc:subject xml:lang="en">Naive Bayes, Confusion Matrix, Data Mining, Algorithm.</dc:subject>
	<dc:description xml:lang="en">One of the primary issues that the government of a nation concentrates on is poverty. The provision of precise and focused data on poverty is a crucial component of the Poverty Reduction Strategy. One technique for classifying data is Naïve Bayes. The aid manager will subsequently use the categorization findings to inform judgments about categorizing and determining who should get basic food assistance. Predictions for those who get basic food assistance fall into two categories: eligible and ineligible. Sample data from the hamlet of XYZ used as the basis for the forecast. In this study, a web-based application is used to construct and assess the Naïve Bayes method. The accuracy for 135 training data, 40 test data, and seven characteristics employed generates 86 percent accuracy, 85 percent recall, and 88 percent precision according to the assessment findings using the confusion matrix.</dc:description>
	<dc:publisher xml:lang="en">Pandawan Sejahtera Indonesia</dc:publisher>
	<dc:date>2022-09-21</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://iiast.iaic-publisher.org/ijcitsm/IJCITSM/article/view/114</dc:identifier>
	<dc:identifier>10.34306/ijcitsm.v2i2.114</dc:identifier>
	<dc:source xml:lang="en">International Journal of Cyber and IT Service Management; Vol. 2 No. 2 (2022): October; 154-162</dc:source>
	<dc:source>2808-554X</dc:source>
	<dc:source>2797-1325</dc:source>
	<dc:source>10.34306/ijcitsm.v2i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://iiast.iaic-publisher.org/ijcitsm/IJCITSM/article/view/114/47</dc:relation>
	<dc:relation>10.34306/ijcitsm.v2i2.114.g47</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Yamato Shino, Yusuf Durachman, Nana Sutisna.</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0/</dc:rights>
</oai_dc:dc>