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Journal : Innotech

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 Vol 2 No 1 (2025): Innotech Issue Januari 2025
Publisher : Universitas Siber Indonesia

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Abstract

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.
PEMANFAATAN BUSINESS INTELLIGENCE UNTUK MONITORING TREN PENYAKIT DI RUMAH SAKIT Supriono, Agus; Herwanto
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 1 No 2 (2024): Innotech Issue Juli 2024
Publisher : Universitas Siber Indonesia

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Abstract

In the digital transformation era, hospitals are increasingly aware of using information technology, mainly through the Hospital Information System (HIS), to enhance efficiency, service quality, and data-driven decision-making. The primary focus on developing Business Intelligence (BI) enables hospitals to gain profound insights from data, especially regarding the quantity and distribution of diseases in the service area. This research discusses the urgency of leveraging BI to improve disease management, prevention, and evidence-based policies. In addressing the complexity of risk management in healthcare services, BI supports the design of prevention strategies, efficient resource allocation, and responses to public health needs. The research method focuses on BI development using SIM RS data related to the quantity and distribution of diseases. Key BI components, such as Data Warehouse, Business Analytics, Data Mining, and Business Performance Management, are utilized to achieve research objectives. The Extract, Transform, Load (ETL) process forms the foundation for optimizing the transformation of operational data into meaningful information. The research results indicate that implementing BI, mainly through the Power BI platform, clearly visualizes patient characteristics, demographics, and disease distribution. Management can make better decisions, improve service efficiency, and enhance patient experience. This study contributes to understanding the importance of utilizing HIS data through BI to enhance healthcare service quality and support strategic hospital decision-making.