APPLICATION OF ENSEMBLE LEARNING TO ONLINE RECRUITMENT FRAUD DETECTION
The development of internet usage has influenced many recruitment processes, but it has also brought negative impacts in the form of an increase in online recruitment fraud cases. To address this challenge, this research aims to develop a machine learning-based Online Recruitment Fraud Detection...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/83146 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The development of internet usage has influenced many recruitment processes, but
it has also brought negative impacts in the form of an increase in online recruitment
fraud cases. To address this challenge, this research aims to develop a machine
learning-based Online Recruitment Fraud Detection System. The approach taken
involves the use of an ensemble learning model that combines the CatBoost
Classifier algorithm and the BERT Natural Language Processing (NLP) model. The
two algorithms will be trained separately, then the probability results will be
combined using the weighted voting method to obtain the final probability. The
development of this system follows the CRISP-DM (Cross-Industry Standard
Process for Data Mining) methodology, which ensures systematic steps in model
development. The main objective of this research is to compare the performance of
each algorithm with the performance of the ensemble model, and determine the best
approach in building an Online Recruitment Fraud Detection System. The expected
result is the creation of a model with a high level of precision and a tolerable recall
value in identifying fake job vacancies online. This research is expected to
contribute significantly to the development of effective solutions to the challenges
of recruitment fraud in the online context. |
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