Diagnosa Tingkat Depresi Mahasiswa Selama Masa Pandemi Covid-19 Menggunakan Algoritma Random Forest

Abstract

Covid-19 virus has become a pandemic across the world, including Indonesia. Based on the data from the Covid-19 Handling Officer Unit, the number of Covid-19 sufferers in Indonesia until February 15, 2021 reaches 1.2 million people . The n umber of daily cases that continues to grow has forced the government to enforce policies to work, study, and worship from home to minimize the Covid-19 transmission. T h e policy and many Covid-19 sufferers Indonesia affect the mental health of people, including students of Singaperbangsa Karawang University. Therefore , this research aims to diagnose the initial level of depression in students of Singaperbangsa Karawang University during Covid-19 pandemic by using data mining with Random Forest algorithm. The m ethod used in this research is KDD (Knowledge Discovery in Database) with data used come from PHQ-9 questionnaire given to 392 respondents according to calculation of Slovin formula. Evaluation model used is 10-fold cross validation, with accuracy, sensitivity and specificity parameters. The results of the research show the depression level prediction model using Random Forest algorithm has an accuracy of 85.94% . From 392 students, 1.02% of students are normal, 47.96% have mild depressive symptoms, 36.73% have mild depression, 8.16% have moderate depression, and 6.12% have major depression.