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

Hary Sabita (Fakultas Ilmu Komputer, Informatics & Business Institute Darmajaya)
Fitria Fitria (Fakultas Ilmu Komputer, Informatics & Business Institute Darmajaya)
Riko Herwanto (Fakultas Ilmu Komputer, Informatics & Business Institute Darmajaya)

Jurnal Informatika
Vol 21, No 1 (2021): Jurnal Informatika

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