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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Bila, Nida Aulia Salsa
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Computer Science & IT Decision Sciences, Operations Research & Management

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Perbandingan Naїve Bayes Classifier Dan Support Vector Machine Dalam Mengklasifikasikan Tingkat Pengangguran Terbuka Di Indonesia Dewi, Dhita Diana; Kharisma, Ivana Lucia; Bila, Nida Aulia Salsa
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.811

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%.
Co-Authors Dewi, Dhita Diana Kharisma, Ivana Lucia