文章基本信息

标题：Penerapan Metode Artificial Neural Network dalam Peramalan Jumlah Kunjungan Ibu Hamil (K4)
本地全文：下载
作者：Ananda Riska Mita Izati ; Hari Basuki Notobroto
期刊名称：Jurnal Biometrika dan Kependudukan
印刷版ISSN：2302-707X
电子版ISSN：2540-8828
出版年度：2019
卷号：8
期号：1
页码：11-20
DOI：10.20473/jbk.v8i1.2019.11-20
出版社：Universitas Airlangga
摘要：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.
其他摘要：Forecasting is a systematic attempt to predict future events using past data, based on scientific and qualitative methods. For the maternal health program, forecasting is important as its process consist s 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 antenatal care (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 to 2015 obtained from the Provincial Health Office of East Java, with a case study in Bondowoso. The forecasting result in 2016 based on the 12-4-1 network architecture was 9533.5698, with the value of Mean Square Error (MSE) of 3091.84404. The average percentage of error d based on a comparison with the actual data is 0.1854 or reaching the accuracy of 99.8 1 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.
关键词：forecasting; arti cial neural network; antenatal care
其他关键词：forecasting; artificial neural network; antenatal care