Blood donation is an important process that involves taking some blood from a donor to donate and store it in a blood bank. This blood can then be used for transfusions for those in need, such as in cases of accidents, major surgery, or certain illnesses. In Indonesia, blood donation is very important, especially because of the high maternal mortality rate due to bleeding during childbirth. The need for blood continues to increase as the number of accidents and illnesses requiring blood transfusions increases. Although blood donation has great benefits for the recipient, many people do not realize that donating blood regularly also brings various health benefits to the donor. This study aims to increase public understanding about the importance of blood donation through interviews and research at Sunan Giri University, Surabaya. Interview results show that blood donation not only saves lives, but also helps maintain the health of donors and encourages a spirit of mutual cooperation in society. Through education and increasing awareness, more people are expected to be involved in sustainable blood donation activities.

Coronavirus (Covid-19) is a disease that belongs to a large family of disorders that can cause mild to severe symptoms. The coronaviruses Middle East Respiratory Syndrome (MERS) and Serious Acute Respiratory Syndrome (SARS) are two types of coronaviruses that cause severe illness (SARS). According to WHO estimates as of December 17, 2021, Covid-19 has infected about 271,963,258 individuals, with a death rate of 5,331,019 cases. Hospitals only have a limited number of Covid-19 test kits because of the daily increase in cases. As a result, to prevent the spread of Covid-19 among persons, it is necessary to develop an automatic detection method as quickly as feasible, as well as other diagnosis options. The goal of this research was to employ GLCM to extract features and the Backpropagation Neural Network classification technique to automatically develop a Covid-19 diagnosis system by classifying the lungs into two groups: normal lungs and Covid-19 lungs. Pre-processing, segmentation, feature extraction, and classification were all used in this study's lung classification method. The accuracy of the test findings was assessed to be 85%. The sensitivity value for the normal class was 92.5%, whereas the sensitivity value for the Covid-19 class was 77.5%. The specification value for the normal class was 22.5%, whereas the specification value for the covid-19 class was 7.5%. It may be deduced from the accuracy, sensitivity, and specification percentages that the developed system is capable of categorizing lungs using X-Ray lung pictures.