Background: Postpartum mothers are the vulnerable population when exposure by the COVID-19. Transmission of the COVID-19 can cause a change in the breastfeeding process and has an impact on the mother's psychology. However, little documented experience of breastfeed of mothers who are infected with COVID-19 in Indonesia. Objectives: The aim of study was to explore the experience of breastfeeding of mothers who have a history of being infected with COVID-19 Methods: A qualitative method with a phenomenological approach was used in this study. A total of 12 postpartum mothers who were history infected with COVID-19. Participants were recuited by purposive sampling. Data collection, in Lebak, Banten Province from April to May 2022, was performed by in-depth interviews, assisted by interview guidelines, recording devices, note-taking equipment, and field notes. The data were analyzed by the Colaizzi method Results: The result of this study are the feelings of breastfeeding mothers when infected with COVID-19 consists of two sub-themes: (1) feelings sadness, (2) feelings fear. The breastfeeding experience of mothers infected with COVID-19 there are 4 sub-themes: (1) how to provide nutrition to babies, (2) the frequency of breastfeeding babies, (3) health protocols carried out by mothers, (4) sources of information related to breastfeeding that mother got. The support person during difficult times obtained several sub-themes: (1) support from husbands, (2) support from parents, (3) support from in-laws (4) support from family, (5) support from friends or relatives, (6) support from neighbors, (7) support from health workers. Conclusion: COVID-19 infection tends to impact to psychological aspect among breastfeeding mothers. Support from significant persons, relative and health workers needed for successful breastfeeding during and after being infected with COVID-19.

The Grey Wolf Optimizer (GWO) is a relatively new population-based optimizer. Various optimization problems have been solved using GWO. This paper presents an experiment on how to apply GWO to solve the minimum spanning tree (MST) problem. MST is normally solved using revision strategy when formulating the fitness in other population-based algorithms. Another strategy, called Penalty strategy, is used in the experiment. Existing dataset for MST problem is tested using GWO. The experiment showed that implementation of penalty strategy in the fitness function of GWO can find a solution with almost 96% accuracy.

Halal food has increasing demand in recent years. Many countries are promoting its tourism and culinary by providing halal status. In order to implement Halal system which is used to obtain Halal status, the culinary or restaurant company should accomplish several requirements that covers from many stakeholders in their supply chain management such as supplier, logistics, and production process. Hence, there are some risks obtained in conducting Halal supply chain system. The risks found at this research are from its business process which is procurement, raw material delivery, inventory, production, and consumer. From those processes, the risks discovered are listed and assigned with their severity and probability, ended up in Risk Priority Index. The risks then ranked and mapped in the risk map in order to know which risks that has high impacts. Some responses are proposed to overcome the critical risks, such as sanitary checking before delivered to the customer, make some chambers, and make SOP related to hygiene of the kitchen.