# Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia

arXiv: https://arxiv.org/abs/2304.02746 · HTML: https://mlsanity.com/paper/2304.02746/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages](https://mlsanity.com/paper/2205.15960/) (2022)
- [IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP](https://mlsanity.com/paper/2011.00677/) (2020)
- [IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation](https://mlsanity.com/paper/2104.08200/) (2021)
- [Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector](https://mlsanity.com/paper/2009.05720/) (2020)
- [Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation](https://mlsanity.com/paper/2009.09309/) (2020)
- [Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging](https://mlsanity.com/paper/1809.03391/) (2018)

## Abstract (arXiv)

This study provides an overview of the history of the development of Natural Language Processing (NLP) in the context of the Indonesian language, with a focus on the basic technologies, methods, and practical applications that have been developed. This review covers developments in basic NLP technologies such as stemming, part-of-speech tagging, and related methods; practical applications in cross-language information retrieval systems, information extraction, and sentiment analysis; and methods and techniques used in Indonesian language NLP research, such as machine learning, statistics-based machine translation, and conflict-based approaches. This study also explores the application of NLP in Indonesian language industry and research and identifies challenges and opportunities in Indonesian language NLP research and development. Recommendations for future Indonesian language NLP research and development include developing more efficient methods and technologies, expanding NLP applications, increasing sustainability, further research into the potential of NLP, and promoting interdisciplinary collaboration. It is hoped that this review will help researchers, practitioners, and the government to understand the development of Indonesian language NLP and identify opportunities for further research and development.