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Penerapan Metode Artificial Neural Network dalam Peramalan Jumlah Kunjungan Ibu Hamil (K4)

Jurnal Biometrika dan Kependudukan · Vol. 8 No. 1 (2019) · pp. 11–20

DOI
10.20473/jbk.v8i1.2019.11-20
ISSN
2540-8828, 2302-707X
Published
2019-07-17
Publisher
Universitas Airlangga
Language
eng

Abstract

Forecasting is a systematic attempt to predict future events usingpast data, based on scientific and qualitative methods. For the maternal health program, forecasting is important as its process consists of planning, targetting and achievement. Based on data from the Ministry of Health, the quality of antenatal care in Indonesia was still low (87.48 percent) compared to that of the national target (95 percent). This study aims to apply the methods of artificial neural network in predicting the antenatalcare (K4). This applied research used a descriptive method with secondary data in the form of monthly antenatal care visits (K4) from the year of 2012 to2015 obtained from the Provincial Health Office of East Java, with a case study in Bondowoso. The forecasting result in 2016 based onthe 12-4-1 network architecture was 9533.5698, with the value of Mean Square Error (MSE) of 3091.84404. The average percentage of errord based on a comparison with the actual data is 0.1854 or reaching the accuracy of 99.81 percent. The conclusion of this study is that a neural network has a low error value and a high accuracy.Therefore, forecasting results can be used as an input in the planning program.

Keywords

Collaboration network of the authors

40 authors · 139 co-authorship links · showing the 40 most connected

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  • Universitas Airlangga
  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ririh YudhastutiAffiliation unknown4
Kuntoro KuntoroAffiliation unknown3
Lilis SulistyoriniAffiliation unknown3
Rachmah IndawatiAffiliation unknown2
Farapti FaraptiUniversitas Airlangga2
Khuliyah Candraning DiyanahUniversitas Airlangga2
Aditya Sukma PawitraUniversitas Airlangga2
Muhammad Rasyid RidhaAffiliation unknown2
Rahmat DapariAffiliation unknown2
Diva Alishya ShafwahAffiliation unknown2
Babucarr JasseyAffiliation unknown2
Nafi'atul IrbahAffiliation unknown2
David Setyo BudiAffiliation unknown1
Hotmauli SiahaanAffiliation unknown1
Yudi Her OktavionoAffiliation unknown1
Paulus Parholong SiahaanAffiliation unknown1
Rizky Novita AnjaswantiAffiliation unknown1
Dwi - KuswantoAffiliation unknown1
Annisaa Wulida FurqoniaAffiliation unknown1
Anggraini BarlianAffiliation unknown1
Diah RatnadewiAffiliation unknown1
Atik Choirul HidajahAffiliation unknown1
Ika Dewi AnaAffiliation unknown1
Christofora Hanny WidjajaAffiliation unknown1
Rizka Musdalifah AmsarAffiliation unknown1
Triati Dewi Kencana WunguAffiliation unknown1
Fedik Abdul RantamAffiliation unknown1
Ferdiansyah FerdiansyahAffiliation unknown1
Romaniyanto RomaniyantoAffiliation unknown1
Cita Rosita Sigit PrakoeswaAffiliation unknown1
Damayanti TinduhAffiliation unknown1
Dwikora Novembri UtomoAffiliation unknown1
Heri SurotoAffiliation unknown1
Mochammad Bagus QomaruddinUniversitas Airlangga1
Apik Indarty MoedjionoHasanuddin University1
Nursalam NursalamAffiliation unknown1
Fery Agusman Motuho MendrofaAffiliation unknown1
Chatarina U. WahyuniAffiliation 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://e-journal.unair.ac.id/JBK/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Penerapan Metode Artificial Neural Network dalam Peramalan Jumlah Kunjungan Ibu Hamil (K4)</dc:title>
	<dc:creator>Izati, Ananda Riska Mita</dc:creator>
	<dc:creator>Notobroto, Hari Basuki</dc:creator>
	<dc:subject xml:lang="en-US">forecasting</dc:subject>
	<dc:subject xml:lang="en-US">artificial neural network</dc:subject>
	<dc:subject xml:lang="en-US">antenatal care</dc:subject>
	<dc:description xml:lang="en-US">Forecasting is a systematic attempt to predict future events usingpast data, based on scientific and qualitative methods. For the maternal health program, forecasting is important as its process consists of planning, targetting and achievement. Based on data from the Ministry of Health, the quality of antenatal care in Indonesia was still low (87.48 percent) compared to that of the national target (95 percent). This study aims to apply the methods of artificial neural network in predicting the antenatalcare (K4). This applied research used a descriptive method with secondary data in the form of monthly antenatal care visits (K4) from the year of 2012 to2015 obtained from the Provincial Health Office of East Java, with a case study in Bondowoso. The forecasting result in 2016 based onthe 12-4-1 network architecture was 9533.5698, with the value of Mean Square Error (MSE) of 3091.84404. The average percentage of errord based on a comparison with the actual data is 0.1854 or reaching the accuracy of 99.81 percent. The conclusion of this study is that a neural network has a low error value and a high accuracy.Therefore, forecasting results can be used as an input in the planning program.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2019-07-17</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://e-journal.unair.ac.id/JBK/article/view/12289</dc:identifier>
	<dc:identifier>10.20473/jbk.v8i1.2019.11-20</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Biometrika dan Kependudukan; Vol. 8 No. 1 (2019): JURNAL BIOMETRIKA DAN KEPENDUDUKAN; 11-20</dc:source>
	<dc:source>2540-8828</dc:source>
	<dc:source>2302-707X</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JBK/article/view/12289/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Jurnal Biometrika dan Kependudukan</dc:rights>
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