Perbandingan metode klasifikasi data mining untuk deteksi keaslian lowongan pekerjaan di medsos

Fajar, Mohammad Malik, Putri, Annisa Rizkiana and Holle, Khadijah Fahmi Hayati ORCID: (2022) Perbandingan metode klasifikasi data mining untuk deteksi keaslian lowongan pekerjaan di medsos. Jurnal Ilmiah Informatika, 7 (1). pp. 41-48. ISSN 2549-7480

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The COVID-19 pandemic has resulted in more and more people losing their jobs. Due to layoffs or bankrupt companies. This has resulted in many people looking for job vacancies. Job vacancies are circulating on social media but there are real and fake ones. Irresponsible people create job vacancies on social media with fraudulent purposes or for personal gain. So, a comparison of data mining classification methods was made for the detection of authenticity of job vacancies on social media. The method used is naive bayes, KNN, and decision tree. In order to find out which method has the highest accuracy value and can be used to classify the authenticity of job vacancies, and fraud on social media can be prevented. Based on this research, the method that has the highest accuracy value is the KNN method. The accuracy value is 94.93%, while the Decision Tree model has an accuracy value of 91.57% and the Naive Bayes model has an accuracy of 84.35%. The KNN method is the best method for classifying the authenticity of job vacancies.

Item Type: Journal Article
Subjects: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Divisions: Faculty of Technology > Department of Informatics Engineering
Depositing User: Khadijah Fahmi Hayati Holle
Date Deposited: 09 Jun 2023 06:20


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