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The development of information and communication technology has been proactively addressed by Muhammadiyah. Muhammadiyah has modernized da'wah methods by utilizing ICT technology in every activity, but it is still limited to media commonly used by the public such as social media, the web on the Internet, and so on, so that it is still limited, partial, and has not been systematic and structured ally serves its people and is constrained in monitoring and evaluation. The use of web based systems can be a solution to develop locations or e-da'wah based on geography while answering problems in the management and branch of the Muhammadiyah region in monitoring and evaluating its activities. The research method used is to use quantitative methods to obtain primary data to find out the user's desire for e-da'wah in Muhammadiyah citizens while the observation method and survey questionnaire are used to evaluate the acceptance of Muhammadiyah member users to the use of e-da'wah applications that have been made. This research aims to design a framework for the concept of web-based e-da'wah applications. As a result, the e-da'wah application model developed on a web-based basis has received responses from 57% of respondents who find it easy to understand the studies presented in this e-da'wah media.

In December 2019, SARS-CoV-2 caused the coronavirus disease (COVID-19) to spread to all countries, infecting thousands of people and causing death. COVID-19 causes mild illness in most cases, although it can make some people seriously ill. Therefore, vaccines are in various phases of clinical progress, and some of them have been approved for national use. The current state of affairs reveals that there is a critical need for a quick and timely solution to the need for a Covid-19 vaccine. Non-clinical methods such as data mining and machine learning techniques can help to do this. This study will focus on US COVID-19 Vaccination Advances using Machine learning classification algorithms and Using Geospatial analysis to visualize the results. The paper's findings indicate which algorithm is better for a given data set. Naive Bayes algorithm is used to run tests on real world data, and is used to analyze and draw conclusions. Period of Accuracy and performance, and it was found that Naive Bayes is very superior in terms of time and accuracy.

We are investigating the evolution of four big US stock market indexes' regular returns after the 2000 technology crash and the 2007-2009 financial crisis. Our approach is based on topological data processing (TDA). To identify and measure topological phenomena occurring in multidimensional time series, we use persistence homology. We obtain time-dependent point cloud data sets using a sliding window, which we connect a topological space for. Our research indicates that a new method of econometric analysis is offered by TDA, which complements the traditional statistical tests. The tool may be used to predict early warning signs of market declines that are inevitable.

Magnetic Resonance Image (MRI) is a medical technique commonly used by radiologists to visualize organ structures in humans without surgery, MRI provides a wealth of information about human soft tissue, which helps to diagnose brain tumors. Classifying normal and abnormal MRI images helps in early detection of brain tumors. Usually classifies MRI images using T2-weighted MRI images. However, to achieve perfect accuracy, sensitivity and specificity is very difficult. Fluid-attenuated inversion recovery (FLAIR) image is superior to T2-weighted image for detecting MS brain lesions. This study aims to classify FLAIR MRI images of the brain by classifying abnormal and normal MRI images based on statistical texture analysis, 44 abnormal datasets of BRATS 2017 training data and 4 normal datasets from Patient Contributed Image Repository (PCIR). At the beginning of the step, pre-processing of the image is followed by the histogram equalization method to extract the feature with statistical texture analysis by calculating the mean, variance, deviation, skewness, kurtosis, energy, entropy and smoothness. Finally, a multinomial logistic regression model with a ridge estimator is used to classify abnormal and normal MRI images and evaluated by k-fold validation. The results obtained from the proposed method of accuracy, sensitivity, and specificity reached 100%. This shows the method used to do a good classification in this case.