Abstract:
Every day, there are issues on many job-calling websites on the
internet. Some of the jobs advertised online are actually fake jobs that
lead to the theft of sensitive information. So it needs to be identified.
Using the term frequency inverse document frequency (TF-IDF) method
and the K-Nearest Neighbor (KNN) algorithm, the system detects fake
job postings. TF-IDF is one of the statistical techniques for calculating
the importance or score of each word in a document. In order to extract
features, TF-IDF is employed. The purpose of this system is to classify
real or fake jobs by using the KNN classifier. This system is implemented
using 17866 jobs as the Fake Job Posts detection dataset. The accuracy of
the proposed system is measured by using a confusion matrix (precision,
recall, and F-Measure). The experiment results have been many times
used with the existing actual data by using K-Nearest Neighbor algorithm
with K value changes (K=1, 3, 5, 7, 9). According to the comparison
results, the proposed system has achieved high accuracy many times.
