PREDIKSI REAL OR FAKE JOB POSTING MENGGUNAKAN METODE LONG SHORT-TERM MEMORY - herwanto; Budiyansyah, Disky Phiter (Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi, 2025)
Nowadays, advances in information technology have made a significant impact, including online job searches. However, the emergence of fake job advertisements poses a serious threat to job seekers, causing the risk of financial loss and misuse of personal data. This research aims to develop a Long Short-Term Memory (LSTM)-based prediction model to distinguish between real and fake job advertisements automatically and accurately. The dataset used is “Real or Fake Job Posting Prediction” from the Kaggle website, which contains job posting data. The research process includes data cleaning, Natural Language Processing (NLP) techniques such as tokenization and lemmatization, and model training using the TensorFlow framework. The resulting model achieved 97.61% accuracy and 0.08% loss rate, showing good performance in identifying patterns in complex text data. The results of this research are expected to help the community, especially job seekers to reduce the risk of job vacancy fraud.