Skripsi

CLINICAL NAMED ENTITY RECOGNITION PADA DATA BIOMEDIS MENGGUNAKAN PRE-TRAINED WORD EMBEDDINGS DAN DEEP LEARNING

---

Penilaian

---

The rapid growth of digital biomedical data has posed significant challenges in managing and extracting information from unstructured medical texts. This study aims to develop and evaluate a Clinical Named Entity Recognition (CNER) model by combining pre-trained word embeddings with deep learning architectures. Three biomedical datasets were used: JNLPBA, NCBI-Disease, and BC2GM. The experiments were conducted in two stages: the first stage compared the performance of GloVe-BiLSTM, ELMo-BiLSTM, and BERT-BiLSTM combinations; the second stage evaluated BERT-BiLSTM and PubMed2MBERTBiLSTM models using fine-tuning and early stopping strategies. Evaluation using macro average precision, recall, and F1-Score shows that contextual embeddings consistently outperform static embeddings, with GloVe yielding the lowest performance. Transformer-based models like BERT and PubMed2MBERT outperform ELMo due to their self-attention mechanism that better captures token relationships. PubMed2MBERT-BiLSTM, pretrained in the biomedical domain, achieved the best performance across all datasets, highlighting the effectiveness of domain-specific models in medical entity recognition.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003420	T175811	T1758112025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1758112025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xviii, 143 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Sistem komputer

Specific Detail Info

-

Statement of Responsibility

MI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment