The National Economic Recovery (NER) Program is one of the responses initiated by the government in Indonesia's economic recovery due to the impact of COVID-19, the target is to reduce the activities of affected communities, including cooperatives. One of the priority aspects for the program to run well and smoothly is the role of institutions in knowledge management and process sharing. This paper examines the role of knowledge management and sharing in cooperatives with qualitative limitations at the knowledge process level, knowledge design level, strategic interaction level, social participation level, academic and scientific ecosystem level, and network and partnership level. A qualitative description becomes a research method with secondary data in the form of a comparison of cooperatives in 2019–2021 as a representation before and during the COVID-19 pandemic. COVID-19 secondary data for 20 months from April 2020 to September 2022 in Indonesia dynamically also support sharpening the analysis. The source of cooperative data is from the publications of the Ministry of Cooperatives and SMEs, while the source of COVID-19 data comes from the publication of the COVID-19 Task Force. The analysis is carried out by building qualitative aspects into quantitative ones that can be formulated in the form of cooperative applications. The result is that the application of the knowledge process level, knowledge design level, strategic interaction level, social participation level, academic and scientific ecosystem level, and network and partnership level can improve decision-making, capture, share, and measure institutional knowledge for the success of the NER Program.

Retinal vessel segmentation is part of the morphological extraction of retinal blood vessels that plays an essential role in medical image processing. Manual segmentation is possible to do, but it is time-consuming and requires special operators. Moreover, the possibility of variability between operators is vast. This study aims to answer the shortcomings of the manual segmentation process by automatically segmenting retinal blood vessels. The main contribution of this study is the use of a simple method to iteratively segment retinal blood vessels.  All processes in the segmentation are simulated using Matlab. The algorithm was evaluated by comparing the results of the automatic segmentation with 20 manually segmented images from the STARE dataset. The result show specificity 98.13%, accuracy 93.60%, sensitivity 56.42%, precision 80.48%, and the dice coefficient 64.06%. In conclusion, the automatic retinal blood vessel image segmentation process worked well.

The regulation and supply of oxygen as one of the medical gases in the hospital is important to ensure the availability of these gases for the survival of patients. The regulation of oxygen gas in hospitals usually uses a piping system with manifolds. The manifold will monitor the oxygen gas pressure on each tube. Manifold systems that are widely used in general can only monitor pressure but cannot perform an automatic exchange on gas cylinders if the pressure is under the permissible conditions. The manifold system design developed is equipped with pressure monitoring for automatic exchange of oxygen gas cylinders using pressure sensors and microprocessors.  The test results of the system using regulator and barometer comparisons showed the percentage value of sensor pressure accuracy of 96.92 percent and 97.16 percent. At pressure below the limit of 285 KPa manifold can perform the exchange of active gas cylinders automatically. These results show the manifold design built can work quite well.

Indonesia has a large population in the world, and this is both an advantage and a challenge. The importance of recording and monitoring nutrition from the age of toddlers is a form of intervention to create a productive population. Integrated Healthcare Center or posyandu is community empowerment to monitor nutrition independently and sustainably. The pandemic caused by COVID-19 also affects the implementation of posyandu in Indonesia, one of which is in densely populated areas, namely Kelurahan Duri Selatan Jakarta Barat. This community service activity aims to apply mobile-health technology so that the nutrition monitoring activities of toddlers in posyandu continue to run during this pandemic. The technology used in this community service activity is a mobile-health application based on android, which is a form of application of technology from research. The method used is the provision of material online, then conducting assistance implementation practice. Evaluation of the results was achieved by applying pre-test and post-test to posyandu cadres and citizens. The evaluation results showed increased knowledge of posyandu cadres and residents after community service activities. This community service activity provides an alternative application of new technologies for community empowerment in improving nutritional status.

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