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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) · hlm. 11–20

DOI
10.20473/jbk.v8i1.2019.11-20
ISSN
2540-8828, 2302-707X
Terbit
2019-07-17
Penerbit
Universitas Airlangga
Bahasa
eng

Abstrak

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.

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  • Universitas Airlangga
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Ririh YudhastutiAfiliasi tidak diketahui4
Kuntoro KuntoroAfiliasi tidak diketahui3
Lilis SulistyoriniAfiliasi tidak diketahui3
Rachmah IndawatiAfiliasi tidak diketahui2
Farapti FaraptiUniversitas Airlangga2
Khuliyah Candraning DiyanahUniversitas Airlangga2
Aditya Sukma PawitraUniversitas Airlangga2
Muhammad Rasyid RidhaAfiliasi tidak diketahui2
Rahmat DapariAfiliasi tidak diketahui2
Diva Alishya ShafwahAfiliasi tidak diketahui2
Babucarr JasseyAfiliasi tidak diketahui2
Nafi'atul IrbahAfiliasi tidak diketahui2
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Dwikora Novembri UtomoAfiliasi tidak diketahui1
Heri SurotoAfiliasi tidak diketahui1
Mochammad Bagus QomaruddinUniversitas Airlangga1
Apik Indarty MoedjionoHasanuddin University1
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Sumber metadata: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JBK/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (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>
</oai_dc:dc>