Clustering of Earthquake and Volcanic Eruption Trauma Survivor Groups using K-Means Algorithm

Abstract

Prolonged trauma conditions in a person have the potential to become Post-Traumatic Stress Disorder (PTSD). The American Psychological Association (APA) defines PTSD as an experience of someone who experiences a traumatic event that can cause disturbances in self-integrity, feeling of helplessness and specific trauma. People who are directly affected by earthquakes and volcanic explosions generally experience trauma. In order to reconcile the survivors of trauma, this study will categorize trauma survivors from Mount Sinabung's eruption in Karo Regency and the Ambon City Earthquake according to their level of trauma. The clustering process uses the Data Mining, a method to extract and identify trauma survivor data in order to produce the required information. The algorithm on K-Means is used in the computational process. The algorithm on K-Means has advantages in computational efficiency and ease of use. The data collection instrument in this study used the Impact of Even Scale-Revised (IES-R) Questionnaire which offered a common language and standard criteria for the classification of mental disorders. Information on the trauma survivors cluster of Mild, Moderate, and Severe trauma levels will be generated using the computational and iteration process supported by the Orange application. The results of the research on grouping trauma survivors using the K-means algorithm with the support of an application that helps the iteration process of survivor data processed through the IER-S questionnaire provide information that is useful for healing trauma survivors.