Exclusive breastfeeding among Indonesian working mothers: does early initiation of breastfeeding matter?

BACKGROUND: Early initiation of breastfeeding (EIBF) is a starting point that lays the foundation for breastfeeding and bonding between mother and baby. Meanwhile, working mothers are one of the vulnerable groups for the success of exclusive breastfeeding (EBF). The study analyzed the role of EIBF on EBF among Indonesian working mothers. METHODS: The cross-sectional study examined secondary data from the 2021 Indonesian National Nutritional Status Survey. The study analyzed 4,003 respondents. We examined EBF practice as an outcome variable and EIBF as an exposure variable. We included nine control variables (residence, maternal age, marital, education, prenatal classes, wealth, infant age, sex, and birth weight). All variables were assessed by questionnaire. The study employed a binary logistic regression test in the last stage. RESULTS: The result showed that the proportion of EBF among working mothers in Indonesia in 2021 was 51.9%. Based on EIBF, Indonesian working mothers with EIBF were 2.053 times more likely than those without to perform EBF (p < 0.001; AOR 2.053; 95% CI 2.028-2.077). Moreover, the study also found control variables related to EBF in Indonesia: residence, maternal age, marital, education, prenatal classes, wealth, infant age, sex, and birth weight. CONCLUSION: The study concluded that EIBF was related to EBF. Indonesian working mothers with EIBF were two times more likely than those without to perform EBF. The government needs to release policies that strengthen the occurrence of EIBF in working mothers to increase EBF coverage.

Comparative analysis of deep learning models for detecting face mask.

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-the-art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification.