KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL

In the health sector particularly in view of Maternal and Child Health, predicts a high risk event (resti) the emergence of risk pregnancies that can be addressed at an early stage will greatly affect the decline in Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR). With models such as B...

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Những tác giả chính: , ARIS NUGROHO, , Prof. Drs. Subanar, Ph.D.
Định dạng: Luận văn
Được phát hành: [Yogyakarta] : Universitas Gadjah Mada 2013
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ETD
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author , ARIS NUGROHO
, Prof. Drs. Subanar, Ph.D.
author_facet , ARIS NUGROHO
, Prof. Drs. Subanar, Ph.D.
author_sort , ARIS NUGROHO
collection UGM
description In the health sector particularly in view of Maternal and Child Health, predicts a high risk event (resti) the emergence of risk pregnancies that can be addressed at an early stage will greatly affect the decline in Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR). With models such as Bayesian classification approach with Naïve Bayes HMAP (Maximum A Posteriori hypothesis) which will be used to predict birth experienced by pregnant women with maternal age characteristics, Height, Total hemoglobin, blood pressure, and pregnancy history and congenital disease. All data used discritization based limits and the Department of Health in the form of results predicted probability of the risk, can be used as a reference to place of birth or the performance appraisal of delivery service providers. With klasifNB function in R language through training phase for maximum likelihood estimation and in prediction phase according to criteria / characteristics of pregnant women , a dynamic application to predict corresponding selected area.
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spelling oai:generic.eprints.org:1258672016-03-04T08:24:16Z https://repository.ugm.ac.id/125867/ KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL , ARIS NUGROHO , Prof. Drs. Subanar, Ph.D. ETD In the health sector particularly in view of Maternal and Child Health, predicts a high risk event (resti) the emergence of risk pregnancies that can be addressed at an early stage will greatly affect the decline in Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR). With models such as Bayesian classification approach with Naïve Bayes HMAP (Maximum A Posteriori hypothesis) which will be used to predict birth experienced by pregnant women with maternal age characteristics, Height, Total hemoglobin, blood pressure, and pregnancy history and congenital disease. All data used discritization based limits and the Department of Health in the form of results predicted probability of the risk, can be used as a reference to place of birth or the performance appraisal of delivery service providers. With klasifNB function in R language through training phase for maximum likelihood estimation and in prediction phase according to criteria / characteristics of pregnant women , a dynamic application to predict corresponding selected area. [Yogyakarta] : Universitas Gadjah Mada 2013 Thesis NonPeerReviewed , ARIS NUGROHO and , Prof. Drs. Subanar, Ph.D. (2013) KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=66048
spellingShingle ETD
, ARIS NUGROHO
, Prof. Drs. Subanar, Ph.D.
KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL
title KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL
title_full KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL
title_fullStr KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL
title_full_unstemmed KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL
title_short KLASIFIKASI NA�VE BAYES UNTUK PREDIKSI KELAHIRAN PADA DATA IBU HAMIL
title_sort klasifikasi naa�ve bayes untuk prediksi kelahiran pada data ibu hamil
topic ETD
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