IDENTIFICATION OF MENTAL HEALTH WORKERS IN LAMONGAN WITH MACHINE LEARNING

Abstract

COVID-19 has caused a global health crisis, with increasing numbers of people being infected and dying every day. Various countries have tried to control its spread by applying the basic principles of social aggregation and testing. Experts agree that physical and mental health are interrelated and must be managed and balanced. The government must pay attention to balancing physical and mental health during a pandemic. The Ministry of Health has issued a guidebook for Mental Health and Psychosocial Support (DKJPS) during the COVID-19 pandemic. Based on the mental health conditions of the community or medical personnel, we are trying to create a system for mental health analysis for medical professionals based on the results of questionnaires using the machine learning method (Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression). A total of 24 question questionnaires were submitted to respondents. This study aimed to create a machine learning model (Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression) to identify the mental health of medical personnel during the COVID-19 pandemic. The results of this study are machine learning models that have the highest accuracy in identifying health workers' mental health and are 100% SVM.