The first-ever vast natural language processing benchmark for Indonesian Language. We provide multiple downstream tasks, pre-trained IndoBERT models, and a starter code! (AACL-IJCNLP 2020)

IndoLEM is a comprehensive Indonesian NLU benchmark, comprising three pillars NLP task: morpho-syntax, semantic, and discourse. Presented in COLING 2020.

The first large-scale summarization corpus for the Indonesian language. AACL 2020.

indoBERT Base-Uncased fine-tuned on Translated Squad v2.0

DAC Unpad 2021 Final, predicting government sentiment analytics topic of PPKM COVID-19 Policy

A fine tuned IndoBERT model for University Sentiment On Social Media

Model analisis sentimen berbasis IndoBERT yang dapat memprediksi 6 jenis emosi dalam suatu kalimat, yaitu marah, sedih, senang, cinta, takut, dan jijik.

Just an example of how to use indobenchmark transformer (IndoBERT, IndoGPT, IndoBertTweet) in hugging face.

Web-based hoax detection using IndoBert Fine-tuned model

A simple implementation of IndoBERT model REST API for University Sentiment Analysis using FAST API

NusaBERT: Teaching IndoBERT to be multilingual and multicultural!

Model analisis sentimen berbasis IndoBERT yang dapat memprediksi sentimen dalam teks berbahasa Jawa Ngoko Lugu

This repository contains the final project (skripsi) for sentiment classification on Indonesian Twitter data using the hashtag #KaburAjaDulu. It explores the performance comparison between a fine-tuned IndoBERT model and traditional machine learning models (such as SVM with IndoBERT embeddings). Built with 🤗 Hugging Face Transformers.

dataset came from scraping in play store livin by mandiri app

🥈🏆 SEPAKAT - Modul Integrasi is a winning project in Regsosek Hackathon 2022 organized by The Ministry of National Development Planning/Bappenas Indonesia. This module provides a single individual identification model by integrating Regsosek data as basic information which is then linked with related data using the idea of entity resolution.

Analyzing last 5 years of Tokopedia’s Google Play reviews to uncover product issues, sentiment trends, and recurring user feedback, supported by a semantic search/RAG system for insight retrieval.

No description provided.

IndoBERT is used for sentiment analysis of product reviews, helping businesses understand customer opinions. With fine-tuning, the model improves sentiment classification accuracy, enabling more effective marketing strategies such as ad personalisation, quick response, and service improvement based on customer feedback.

A simple and interactive Streamlit web app to classify Indonesian text sentiment (positive, negative, or neutral) using IndoBERT, a pre-trained BERT model fine-tuned for sentiment analysis.

Insightly.ai is a high-performance backend service designed to transform raw customer feedback into actionable business strategies.

End-to-end Sentiment analysis project using natural language processing (NLP) to analyze reviews of cellular operator applications in the Google App Store.

Predicting news impressions on X (Twitter) using IndoBERT embeddings + XGBoost/LightGBM/CatBoost. Full NLP pipeline: Indonesian text preprocessing, DBSCAN outlier handling, 10-fold CV, Optuna tuning, and Streamlit deployment. Undergraduate thesis project.

A Telegram bot built with Python that enables anonymous chatting between users while incorporating toxic content detection to prevent malicious or harmful messages.

End-to-end pipeline for collecting, preprocessing, and analyzing app reviews to determine user sentiment using Python.

Proyek ini berfokus pada analisis demografis pengguna Twitter selama Pemilihan Presiden Indonesia 2019 dengan menggunakan teknik BERT. Studi ini bertujuan untuk memahami perbedaan opini politik berdasarkan demografi dan mengeksplorasi pengaruh media sosial, khususnya Twitter, terhadap lanskap politik Indonesia.

Real‑time Indonesian Sentiment Analysis powered by IndoBERT, FastAPI, and Next.js

Insightly.ai is a specialized customer feedback analysis platform designed to transform raw qualitative data into actionable business strategies. By leveraging a dual-engine AI approach—IndoBERT for precise sentiment detection and Google Gemini for strategic reasoning—the application provides deep contextual understanding of customer voices.