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A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND NAIVE BAYES CLASSIFICATION USING UNBALANCED DATA HANDLING

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 3 (2023) · pp. 1585–1594

DOI
10.30598/barekengvol17iss3pp1585-1594
ISSN
2615-3017, 1978-7227
Published
2023-09-30
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Classification is a supervised learning method that predicts the class of objects whose labels are unknown. Classification in machine learning will produce good performance if it has a balanced data class on the response variable. Therefore, unbalanced classification is a problem that must be taken seriously. This study will handle unbalanced data using the Synthetic Minority Over-Sampling Technique (SMOTE). The classification methods that are quite popular are the Naïve Bayes Classifier (NB) and the Resilient Backpropagation Artificial Neural Network (Rprop-ANN). The data used comes from the Health Nutrition Research and Development Agency (Balitbangkes) which consists of 2499 observations. This study examines the use of NB and ANN using the SMOTE method to classify the incidence of anemia in young women in Indonesia. Modeling is done on 80% of training data and predictions on 20% of test data. The analysis shows that SMOTE can perform better than not handling unbalanced data. Based on the results of the study, the best method for predicting the incidence of anemia is the Naïve Bayes method, with the sensitivity value of 82%.

Collaboration network of the authors

40 authors · 123 co-authorship links · 25 across different organisations

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

  • IPB University
  • National Research and Innovation Agency
  • State University of Jakarta
  • Politeknik Statistika STIS
  • Universitas Negeri Surabaya
  • University of Bengkulu
  • Badan Pusat Statistik
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anwar FitriantoIPB University4
Anang KurniaIPB University3
Khairil Anwar NotodiputroIPB University3
Kusman SadikIPB University2
Agus Mohamad SolehIPB University2
Andi Harismahyanti A.IPB University2
Reni AmeliaIPB University2
Bagus SartonoIPB University1
Vera Maya SantiState University of Jakarta1
Anik DjuraidahIPB University1
Farah AbqorunnisaIPB University1
Christiana Anggraeni PutriPoliteknik Statistika STIS1
Muhammad Nur AidiIPB University1
Yesan TiaraIPB University1
Rika Sri RachmawatiIPB University1
Khusnia Nurul KhikmahUniversitas Negeri Surabaya1
Leli LatifahIPB University1
Aji Hamim WigenaIPB University1
Titin YuniartyIPB University1
Yudi Fathul AminIPB University1
Herlin FransiskaUniversity of Bengkulu1
Pardomuan Robinson SihombingBadan Pusat Statistik1
Yunna Mentari IndahIPB University1
Megawati MegawatiSunan Gunung Djati State Islamic University Bandung1
Nadira Nisa AlwaniIPB University1
Alfa Nugraha PradanaIPB University1
Dini RamadhaniIPB University1
Iva SarifahState University of Jakarta1
Gusti YarmiAffiliation unknown1
Nunung NurjanahNational Research and Innovation Agency1
Efriwati EfriwatiNational Research and Innovation Agency1
Fitrah ErnawatiAffiliation unknown1
Galih Kusuma AjiAffiliation unknown1
Dian SundariAffiliation unknown1
Fifi RetiatyAffiliation unknown1
Aya Yuriestia ArifinAffiliation 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 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND NAIVE BAYES CLASSIFICATION USING UNBALANCED DATA HANDLING</dc:title>
	<dc:creator>Lestari, Nila</dc:creator>
	<dc:creator>Indahwati, Indahwati</dc:creator>
	<dc:creator>Erfiani, Erfiani</dc:creator>
	<dc:creator>Julianti, Elisa D</dc:creator>
	<dc:description xml:lang="en-US">Classification is a supervised learning method that predicts the class of objects whose labels are unknown. Classification in machine learning will produce good performance if it has a balanced data class on the response variable. Therefore, unbalanced classification is a problem that must be taken seriously. This study will handle unbalanced data using the Synthetic Minority Over-Sampling Technique (SMOTE). The classification methods that are quite popular are the Naïve Bayes Classifier (NB) and the Resilient Backpropagation Artificial Neural Network (Rprop-ANN). The data used comes from the Health Nutrition Research and Development Agency (Balitbangkes) which consists of 2499 observations. This study examines the use of NB and ANN using the SMOTE method to classify the incidence of anemia in young women in Indonesia. Modeling is done on 80% of training data and predictions on 20% of test data. The analysis shows that SMOTE can perform better than not handling unbalanced data. Based on the results of the study, the best method for predicting the incidence of anemia is the Naïve Bayes method, with the sensitivity value of 82%.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-09-30</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/8591</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss3pp1585-1594</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications; 1585-1594</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss3year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8591/6572</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Nila Lestari, Indahwati Indahwati, Erfiani Erfiani, Elisa D Julianti</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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