Klasifikasi Penyakit Gangguan Mental dengan Algoritma LightGBM - Nisa, Khoirun (Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), 2024)
As awareness of the importance of mental health increases in society, it unfortunately has not been fully implemented across various industry sectors, including technology. The Open Source Mental Illness (OSMI) survey data measures the level of awareness of mental health among technology industry workers, a group often overlooked. Therefore, it is necessary to develop a classification model to identify patients, which can help doctors initiate early medical treatment. This study aims to evaluate the effectiveness of the LightGBM algorithm model in terms of its accuracy. The dataset used consists of 1259 data points from the OSMI survey. The study found that the best accuracy ratio was achieved with a 90:10 split. The results indicated a training data accuracy of 93%, while the testing data accuracy was 82%.