Klasifikasi pada Data Fake Job Posting Menggunakan Algoritma Support Vector Machine

Eka, Prasetiya (2023) Klasifikasi pada Data Fake Job Posting Menggunakan Algoritma Support Vector Machine. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

Reporting from the Federal Trade Commission, reports of job fraud and job agencies increased dramatically during the Covid-19 pandemic in America. A total of 5,568 reports on job fraud and job agencies, then increased to 5,982 reports, in the third quarter there was an increase of 1,547 reports from 5982 reports to 7,529 reports. This is the reason why this research topic was raised with the aim of being able to build a model that can classify fake job postings using the Support Vector Machine algorithm. The classes in this classification are divided into 2 categories, namely fraud and not. The classification stages start from literacy studies, data collection, pre-processing, feature extraction, model training, and evaluation. This research produced a classification model with poor performance in classifying data for fraud class and not. The overall accuracy result has an accuracy rate of only 53%. Keywords: Classification, Fake Job Postings, Support Vector Machine.

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