Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 24, 2025Jurnal Pilar Nusa MandiriOpen Access

Comparative Performance of Transformer and LSTM Models for Indonesian Information Retrieval With Indobert

View Full Paper
Ask AI
Bookmark
Share

Authors

NSNendi Sunendar SunendarISIrwansyah Saputra

Discussion

Loading...

Member takes

Overview

This study finds that IndoBERT surpasses LSTM in information retrieval in Indonesian, highlighting the efficiency of transformer models.

Key Points

  • IndoBERT significantly outperformed LSTM in all evaluation metrics for information retrieval tasks.
  • The mean average precision (MAP) for IndoBERT was 0.82 compared to LSTM's MAP of 0.63.
  • Metrics like MAP and Mean Reciprocal Rank (MRR) demonstrate the effectiveness of transformer models for low-resource languages.
  • These findings emphasize the need for advanced neural network models in managing semantic relevance in information retrieval.

Cite This Study

Sunendar et al. (2025) studied this question.

synapsesocial.com/papers/68d6d8ba8b2b6861e4c3f024https://doi.org/10.33480/pilar.v21i2.6920
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1EVALUATION OF INDOBERT AND ROBERTA: PERFORMANCE OF INDONESIAN LANGUAGE TRANSFORMER MODELS IN SENTIMENT CLASSIFICATION2025 · 2 citations
  2. 2Hybrid model: IndoBERT and long short-term memory for detecting Indonesian hoax news2024 · 1 citations
  3. 3Implementation of Text Mining for Evaluating the Relevance Between News Headlines and Content on a Web-Based Platform2025
  4. 4Named entity recognition on Indonesian legal documents: a dataset and study using transformer-based models2024 · 11 citations
  5. 5Incorporation of IndoBERT and Machine Learning Features to Improve the Performance of Indonesian Textual Entailment Recognition2025 · 2 citations