ANALISA DAN PREDIKSI IKLAN LOWONGAN KERJA PALSU DENGAN METODE NATURAL LANGUAGE PROGRAMING DAN MACHINE LEARNING

Abstract

This research was conducted using the data provided by Kaggle. This data contains features that describe job vacancies. This study used location-based data in the US, which covered 60% of all data. Job vacancies that are posted are categorized as real or fake. This research was conducted by following five stages, namely: defining the problem, collecting data, cleaning data (exploration and pre-processing) and modeling. The evaluation and validation models use Naïve Bayes as a baseline model and Small Group Discussion as end model. For the Naïve Bayes model, an accuracy value of 0.971 and an F1-score of 0.743 is obtained. While the Stochastic Gradient Descent obtained an accuracy value of 0.977 and an F1-score of 0.81. These final results indicate that SGD performs slightly better than Naïve Bayes.

Keywords—NLP, Machine Learning, Naïve Bayes, SGD, Fake Jobs