KLASIFIKASI IKLAN LOWONGAN KERJA MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) DAN BORDERLINE SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE (BORDERLINE-SMOTE)

BERLIN, JERRY and Yusliani, Novi and Rodiah, Desty (2024) KLASIFIKASI IKLAN LOWONGAN KERJA MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) DAN BORDERLINE SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE (BORDERLINE-SMOTE). Undergraduate thesis, Sriwijaya University.

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Abstract

The growth of the internet has made it easier to recruit workers through the publication of online job advertisements. However, this convenience also brings the risk of fraud in job advertisements that can harm both job seekers and companies. To overcome this problem, classification of job advertisements is required. One of the main challenges in text classification, especially in job advertisement data, is the significant imbalance of data between the majority and minority classes, with the majority class reaching 3500 data and the minority class only 500 data. This research aims to classify job advertisements using Support Vector Machine (SVM) and Borderline Synthetic Minority Over-Sampling Technique (Borderline-SMOTE) to overcome data imbalance. Tests were conducted to see the effect of job advertisements classification performance using SVM without Borderline-SMOTE and SVM with Borderline-SMOTE, where the parameter C used was 0.1, 0.25, 0.50, 0.75, and 1. The results showed that SVM with Borderline-SMOTE had better performance especially in recall and f-measure. In particular, at setting parameter C = 1 in SVM with Borderline-SMOTE, the most optimal results were obtained, with a precision value of 0.92, recall of 0.80, and f-measure of 0.86.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Borderline Synthetic Minority Over-Sampling Technique, Data Imbalance, Support Vector Machine, Text Classification
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Jerry Berlin
Date Deposited:	06 May 2024 07:17
Last Modified:	06 May 2024 07:17
URI:	http://repository.unsri.ac.id/id/eprint/143696

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