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Stroke is a disease among the top 10 causes of death in the world and Indonesia. Stroke is divided into two, namely ischemic stroke and hemorrhagic stroke. Stroke can happen to anyone at any age with the characteristics of blood vessels experiencing blockage or rupture resulting in a crisis of blood supply that carries oxygen to the brain. The risk factors for stroke are the same as heart disease or other blood vessel diseases such as hypertension, diabetes, and cholesterol. One way to prevent stroke is to minimize several diseases that cause clogged or ruptured blood vessels. This study aims to predict the results of the classification of stroke types based on 6 factors, namely age, gender, cholesterol levels, length of stay (LOS), history, and blood sugar levels of stroke patients. The classification method chosen is one part of machine learning, namely the resilient backpropagation neural network (RBNN) because it is in accordance with the type of data used. The results showed that the prediction results of stroke type classification using six predictors were included in the good category and reached the optimum at the use of 5 nodes in the hidden layer and the resulting error was 9.26. The results of the evaluation of classification predictions using the confusion matrix also get good results, namely with an accuracy of 80% and an F1-score of 82% with a precision of 72% and a sensitivity of 95%. This result will be more optimal if there is the handling of imbalanced data. [ABSTRACT FROM AUTHOR]

The purposes of this research are to build a SIRS model in social media addiction cases; to analyse the model and perform model simulations to prediction the level of social media addiction. This study builds an anlalysis of the SIRS model for Social Media addiction use mathematical graphs, while the model simulation by Maple. The population of this research was 502 active students majoring in mathematics at UNM, with a sample of 145 students obtained using the proportional stratified random sampling technique. The results of this research are the SIRS model of Social Media addiction which is a differential equations system and the model analysis provides an addiction-free and a stable endemic equilibrium point. The results also obtained the value of the basic reproduction number (R0 = 3.6342), which indicates that the level of social media addiction is at an alarming stage, because a student causes 4 other students to become addicted. [ABSTRACT FROM AUTHOR]

This study aims to build a Suspected-Exposed-Infected-Asymptomatic-Recovered (SEIAR) model of the Covid-19 transmission; to obtain a numerical solution for the SEIAR model on the Covid-19 spread in South Sulawesi using the fifth-order Adams-Bashforth-Moulton (ABM) and Runge Kutta (RK) methods. This research is a basic and applied research. The initial values of the variables and parameters used secondary data on Covid-19 cases in South Sulawesi, then the numerical solution was obtained using the fifth-order ABM method and the RK Classical, RK Fehlberg, and RK Merson methods as methods for determining the initial solution. Then compare the three types of the RK method as the best method in determining the initial solution to the ABM method. The results shows that the SEIAR model can be a consideration for the transmission of Covid-19, the results also found that the numerical solution of the SEIAR model by the Classical RK method is better in providing initial solution and prediction of the Covid-19 cases in South Sulawesi. ABM with fewer iterations and smaller errors than RK Fehlberg and RK Merson. These results can be used as a reference for the South Sulawesi government in preventing the spread of Covid-19. [ABSTRACT FROM AUTHOR]