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

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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.

Availability
Inventory Code Barcode Call Number Location Status
2407002645T143696T1436962024Central Library (Referensi)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1436962024
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xvii, VI-2 hlm.; ilus.; tab; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
Text
Media Type
unmediated
Carrier Type
unspecified
Edition
-
Subject(s)
Prodi Teknik Informatika
Borderline SMOTE Technique
Specific Detail Info
-
Statement of Responsibility
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No other version available

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