Klasifikasi Data Kesehatan Mental di Industri Teknologi Menggunakan Algoritma Random Forest - Emia Rosta Br. Sebayang; Yulison Herry Chrisnanto; Melina Melina (IJESPG (International Journal of Engineering, Economic, Social Politic and Government), 2023)
Abstract : Mental health is an integral part of human well-being. Mental health disorders can affect individuals in various aspects of life. Work pressure, heavy workload, and an unhealthy lifestyle can be the main causes of mental health disorders in the workplace, such as industrial technology. Employees' mental health problems in the workplace often do not receive enough attention because they cannot be seen physically. Mental health has a significant impact on the performance that will be shown by employees in contributing to the company, it requires the company's prudence and sensitivity in observing and understanding the mental health conditions of employees. In this study, the Open Source Mental Illness (OSMI) survey data was classified using the Random Forest algorithm with the ensemble method, as well as the bootstrap tree method to improve the performance of the Random Forest algorithm in determining the accuracy of mental health data. The Random Forest algorithm is an ensemble learning method that combines several decision trees to improve prediction accuracy. Classification is carried out using a bootstrap tree which takes training data to train a model or ensemble so that it can take patterns and relations from the data to carry out classification, the Random Forest algorithm is an ensemble learning method that combines several decision trees for research with 80% training data and 20 test data %. The results of this study indicate a fairly good level of accuracy, which is 84%, so that it can make an important contribution in understanding the level of mental health disorders experienced by technology industry employees. The expected results of this research can improve the quality of life and productivity of employees at work.