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Romlah Tantiati
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementasi Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Persalinan Romlah Tantiati; Muhammad Tanzil Furqon; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One reference to measure health services in an area is how medical care is handled by medical personnel. In this case the Maternal Mortality Rate (MMR) as well as in infants is the Infant Mortality Rate (IMR) considered as an important indicator in health care. Antenatal care services are carried out as an effort to prevent complications during pregnancy and expectant mothers by determining the actions that must be given to pregnant women from the examination results so that they are able to suppress the Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR). LVQ network is a competitive training with each output connected in a certain class. In this study the authors implemented LVQ learning to classify normal childbirth into 2 classes, namely whether childbirth is normal or at risk. By using the data collected in the Nursing Care (ASKEP) data on the general physical examination of pregnant women which contains information on age, pelvic size, fetal position, measurement of blood pressure, hemoglobin cell level (HB), results of psychology testing for prospective mothers, Upper Arm Circumference (LILA), proteinurea and Fetal Weight Interpretation (TBJ). The results of LVQ testing for the classification of normal childbirth with learning rate parameters (α) = 0.1, reduction constants LR (c) = 0.1, minimum LR = 10-7 and maxEpoch / iterations maximum 24 times with a comparison of the amount of training data and test data (64:16) is an accuracy value of 93,78%.
Co-Authors Candra Dewi Muhammad Tanzil Furqon