The learning process in Higher Education is currently held in a mixed (hybrid learning) manner, namely face-to-face (offline) and in the network (online) using the case method and team-based project method. Books describing case methods and team-based projects are just one offline illustration of the hybrid learning approach. This research seeks to generate a Pathology and Social Rehabilitation book utilizing the Case Method and Team-Based Project methodologies. The ADDIE model employs development research (research and development) (Analyze, Design, Development, Implementation, and Evaluation). Research participants were undergraduates enrolled in the Department of Educational Guidance and Counseling at State University of Medan. This research found that both language experts' and students' evaluations of the materials' graphic style, usefulness, and overall quality were all high. Furthermore, it is explained at the book design stage, which consists of seven chapters. Each chapter consists of concepts, and theories obtained from the results of routine assignments, critical book reports (CBR), and critical journal reviews (CJR). For mini research, there are concepts of cases, rehabilitation, and solutions, while engineering ideas and projects are included in the conclusions and suggestions in the resulting book. Finally, a book on pathology and social rehabilitation describing the case method model and the team-based project has been published. Thus, it is anticipated that the book will suit the demands of students enrolled in Pathology and Social Rehabilitation courses in the Guidance and Counseling Study Program of the Faculty of Education at Medan State University.

Brain tumor is a condition in which abnormal cells grow unnaturally in the brain. Depending on the size and type, the abnormal cells called tumors can be life-threatening if the patient does not take immediate treatment. The cause of tumor growth in the brain is the presence of risk factors such as family history and ionization radiation. Patients with brain tumors will experience several symptoms of a headache, nausea, memory loss, and changes in vision, speech, and hearing. Detection of brain tumors can be performed with the help of the medical device of Magnetic Resonance Imaging (MRI) Scan. Through the image of MRI Scan results, radiology specialists will interpret and analyze the brain condition. However, analysis and conclusions for this matter take a long period of time. Therefore, a method is required to classify the brain tumors through MRI images automatically. The method used in this research is Counter-propagation Neural Network. Prior to classification, the brain’s MRI image will be used as the input for the image pre-processing stage then go through the segmentation and feature extraction processes. Based on the test, it can be concluded that the proposed method can identify brain tumors with an accuracy of 92.5%.