Analisis Regresi Ordinal Model Logit dan Probit untuk Memprediksi Faktor yang Mempengaruhi Bayi Berat Lahir Rendah - Marius Iban; Diah Indriani (Universitas Airlangga, 2019)
Low birth weight (LBW) has greater risk for experiencing problems. Based on Indonesian DHS data in 2012, the province with the highest infant mortality rate estimate was West Papua amounting to 75 per 1,000 live births. This study used logit and probit regression models to analyze the factors that caused low birth weight. It is thought that by comparing both logit and probit models, the best results could be obtained. The results of research showed that the independent variables that affected babies with LBW were the distance of pregnancy and maternal parity. If the distance of pregnancy was less than 2 years, it would increase the incidence of LBW by 2.7 times (p: 0,00: CI 1b: -4,05;CI ub: -1,50). Moreover, the distance of pregnancy which was less than 2 years would only improve LBW by 19.4 percent, compared with the distance of the pregnancy that was more than 2 years which would increase infant weight by 80.6 percent. As with maternal parity between 0 and more than 4, there was a chance of increasing the incidence of LBW by 1.94 times (p: 0,00; CI 1b: -2,66;CI ub: -1,21). On the other hand, maternal parity of 0 and parity > 4 would improve LBW by 74.2 percent, and a parity of 1 to 4 would only see 25.8 percent improvement. The result of this study suggested that there should be improvement in health promotions, such as family planning cuonseling and consultation for eligible couples.