Perbandingan Naїve Bayes Classifier Dan Support Vector Machine Dalam Mengklasifikasikan Tingkat Pengangguran Terbuka Di Indonesia

Abstract

Unemployment is one of the factors of problems in the economic field, this will have an impact on the balance of the economy. A person can be said to be unemployed if the person does not meet the requirements as a workforce. Open unemployment is a workforce that does not actually have a job. Therefore, this study will classify the Open Unemployment Rate (TPT) in Indonesia in the 2020-2023 period. This study will use the Naїve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. In the SVM algorithm method, for the negative class consists of a precision value of 62%, a recall of 80%, an F1 Score of 70%. While for the positive class consists of a precision value of 87%, a recall of 72%, an F1 Score of 79%. In the NBC algorithm method, for the negative class consists of a precision value of 71%, a recall of 50%, an F1 Score of 59%. While for the positive class consists of a precision value of 76%, a recall of 89%, an F1 Score of 82%. Based on these calculations, the accuracy value of each algorithm has the same accuracy value, which is 75%.