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IMPLEMENTASI MODEL NATURAL LANGUAGE PROCESSING (NLP) PADA SISTEM REKOMENDASI PEKERJAAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS ( CNN) DAN LONG SHORT TERM MEMORY (LSTM)

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Advances in technology present challenges in matching individual skills with appropriate job recommendations. This research develops a Natural Language Processing (NLP) based job recommendation system using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models. The research data is obtained from the Hugging Face and Kaggle public datasets, which include descriptions of work experience and skills. The data was processed through pre-processing stages such as stopword removal, regex, stemming, tokenization, padding, as well as balancing techniques such as SMOTE and undersampling. The dataset is split for model training and testing, with performance measured through accuracy, loss, and validation metrics. Results show that the CNN model on the Hugging face dataset provides the best results, with 95% accuracy, 0.1 loss, 72% validation accuracy, 1.87 validation loss, 79% F1-score, 90% precision, and 72% recall, outperforming LSTM which tends to overfitting. Overall, CNN proved to be more stable and reliable in generalization, especially in handling complex data. This research also confirms that CNN are more effective in processing complex data than LSTM, which tend to be more prone to overfitting, especially on long text data.

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Inventory Code Barcode Call Number Location Status
2507000064T162088T1620882024Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1620882024
Publisher
Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer.,
Collation
xvi, 108 hlm.; ilus.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
Text
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Teknik komputer
Prodi Sistem Komputer, Fakultas Ilmu Komputer
Specific Detail Info
-
Statement of Responsibility
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No other version available

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