Perbandingan Algoritma Klasifikasi Support Vector Machine dan Random Forest pada Prediksi Status Indeks Mitigasi dan Kesiapsiagaan Bencana (IMKB) Satuan Kerja BPS di Indonesia Tahun 2020 - Ayu Aina Nurkhaliza; Arie Wahyu Wijayanto (Jurnal Informatika Universitas Pamulang, 2022)
Natural and non-natural disasters are closely related to material and non-material losses. Government agencies are one of the important elements in disaster mitigation and preparedness efforts in order to reduce the number of victims and losses that will be caused. Disaster preparedness in the work unit is influenced by several factors, including regional characteristics, experience in disasters, education level, and employee conditions. This study aims to obtain a classification method that is able to predict the status of the Disaster Mitigation and Preparedness Index of work units based on several factors that affect disaster preparedness. Data processing uses the R Studio application with the Support Vector Machine (SVM) and Random Forest classification methods. Several studies have shown that the accuracy of the SVM and Random Forest classification methods tends to be better when compared to other classification methods. In addition, SVM is able to classify non-linear data and with Random Forest there will be no overfit as the number of trees increases. The results showed that the Random Forest classification method had higher accuracy, precision, and recall values than SVM with an accuracy value of 78.22%, precision of 75.54%, and recall of 76%.