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Classification Of Maternal Health Risk Using Three Models Naive Bayes Method

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 4 (2023) · pp. 395–404

DOI
10.22146/ijccs.84242
ISSN
2460-7258, 1978-1520
Published
2023-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.

Keywords

Collaboration network of the authors

40 authors · 73 co-authorship links · 14 across different organisations · showing the 40 most connected

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

  • Hasanuddin University
  • Universitas Pancasakti Makassar
  • Universitas Halu Oleo
  • Lambung Mangkurat University
  • Institut Teknologi Bacharuddin Jusuf Habibie
  • Universitas Ahmad Dahlan
  • Indonesia Open University
  • 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
Tona Aurora LubisAffiliation unknown5
Maryati NingsihAffiliation unknown4
Jeffry JeffryInstitut Teknologi Bacharuddin Jusuf Habibie3
Supriyadi La WungoAffiliation unknown2
Daffa Rizqi PrayudyaAffiliation unknown2
Abdul FadlilUniversitas Ahmad Dahlan2
Rusydi UmarAffiliation unknown2
Novita SariAffiliation unknown2
Huntari HarahapAffiliation unknown2
Moh. IhsanAffiliation unknown2
Aulia Syarif AzizAffiliation unknown2
Jafar JafarIndonesia Open University2
Norma NasirAffiliation unknown2
Armin LawiHasanuddin University1
Misbahuddin BuangAffiliation unknown1
Nasruddin NasruddinCenderawasih University1
Siti Hadrayanti AnandaAffiliation unknown1
Muhammad Ikhsan AkbarAffiliation unknown1
Rahmawati RahmawatiAffiliation unknown1
Ramli RamliUniversitas Halu Oleo1
Surdin SurdinUniversitas Halu Oleo1
Susi AstiantihAffiliation unknown1
Salmia SyarifuddinAffiliation unknown1
Yetty YettyAffiliation unknown1
Andi Detti YuniantiHasanuddin University1
Yuyun WidaningsihHasanuddin University1
Asmita AhmadHasanuddin University1
Kidung Tirtayasa Putra PangestuHasanuddin University1
Ashar Ramadan SyahHasanuddin University1
Siti Hilmiati AzyziaAffiliation unknown1
Uswatun KhasanahAffiliation unknown1
Destiati NabilaAffiliation unknown1
Nuraeni NuraeniAffiliation unknown1
Andry PrihartaAffiliation unknown1
Eka WarniAffiliation unknown1
Mira MaisuraAffiliation 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://journal.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Classification Of Maternal Health Risk Using Three Models Naive Bayes Method</dc:title>
	<dc:creator>Mustamin, Nurul Fathanah</dc:creator>
	<dc:creator>Aziz, Firman</dc:creator>
	<dc:creator>Firmansyah, Firmansyah</dc:creator>
	<dc:creator>Ishak, Pertiwi</dc:creator>
	<dc:subject xml:lang="en-US">machine learning; data mining; data analytic</dc:subject>
	<dc:subject xml:lang="en-US">Classification; Maternal Health Risk; Gaussian Naïve Bayes; Multinomial Naïve Bayes; Bournolli Naïve Bayes</dc:subject>
	<dc:description xml:lang="en-US">Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-10-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/84242</dc:identifier>
	<dc:identifier>10.22146/ijccs.84242</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 4 (2023): October; 395-404</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/84242/37711</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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