Analysis of Factors Affecting Birth Weight Using Probabilistic Neural Network (PNN)


  • Ema Pratiwi Universitas Islam Negeri Sumatera Utara



BBL, Probabilistic Neural Network, K-Means Clustering.


The hight mortality rate in newborns is caused by the fact that many babies are born with low birth weight. LBW is one of the factors of infant mortality in Indonesia. Mitra Medika hospital Bandar Kippa is one of the pregnant women who has a Low Birth Weight (LBW) baby. Prevention and treatment of pregnant women when they know that they will give birth to a baby withlow birth weight is very necessary, in order to minimize death during the bieth process. So it is hoped that the existence of a factor analysis that affects birth weight in babies cas help to identify the condition of the baby in pregnant women before the baby is born. In this study, Probabilistic Neural Network (PNN) method was used with 150 data and 7 features including maternal age, maternal weight, maternal height, maternal hemoglobin, gestational distance, parity and maternal education. To get the best accuracy results, training data and testing data are shared using K-Means Clustering. Furthermore, an analysis of the factors that affect BBL using the Probabilistic Neural Network method is carried out, therefore it can be obtained that the probability value affecting BBL is found in the mother’s weight of 0,856 with the highest output layer value in the normal class of 6,741 and an accuracy value of 88,67.


Download data is not yet available.



How to Cite

Ema Pratiwi, “Analysis of Factors Affecting Birth Weight Using Probabilistic Neural Network (PNN)”, J. of Research in Math. Trends and Tech., vol. 3, no. 1, pp. 1-7, Mar. 2021.