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

🥈🏆 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.

dataset came from scraping in play store livin by mandiri app

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

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

Web-based hoax detection using IndoBert Fine-tuned model

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.

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

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

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.

NusaBERT: Teaching IndoBERT to be multilingual and multicultural!

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

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.

A fine tuned IndoBERT model for University Sentiment On Social Media

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

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

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