> For the complete documentation index, see [llms.txt](https://gugugaga.gitbook.io/ptiikinsight/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gugugaga.gitbook.io/ptiikinsight/resources/readme.md).

# Introduction

## What is PTIIKInsight?

<figure><img src="/files/cigDDA71EmzIluNHeQeU" alt=""><figcaption></figcaption></figure>

PTIIKInsight is an AI-powered topic modeling platform designed for analyzing research papers and academic content. Built specifically for JPTIIK (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer), it provides researchers and academics with tools to discover topics and patterns in research literature.

### Our Development Team

* **Muhammad Rajendra Alkautsar Dikna**\
  GitHub: [alkadikna](https://github.com/alkadikna)
* **Rochmanu Purnomohadi Erfitra**\
  GitHub: [nrcst](https://github.com/nrcst)
* **I Kadek Surya Satya Dharma**\
  GitHub: [Suryy16](https://github.com/Suryy16)

## Core Capabilities

### Topic Modeling

* **BERTopic Integration**: Uses pre-trained transformer models for accurate topic discovery
* **Research Paper Analysis**: Specialized for academic content and research papers
* **Text Classification**: Automatically categorize research content by topic
* **Batch Processing**: Efficiently process multiple documents at once

### Data Collection

* **Web Scraping**: Automated collection of research papers from academic sources
* **Data Preprocessing**: Clean and prepare text data for analysis
* **CSV/JSON Export**: Export collected data in standard formats
* **Data Validation**: Ensure data quality before processing

### Web Interface

* **Streamlit Dashboard**: User-friendly web interface for all operations
* **Real-time Predictions**: Interactive topic prediction interface
* **Progress Monitoring**: Track scraping and training operations
* **Results Visualization**: Charts and tables for analysis results

## Use Cases

### Academic Research

* **Literature Analysis**: Analyze trends in research publications
* **Topic Discovery**: Identify emerging research areas and themes
* **Research Classification**: Categorize papers by research domain
* **Content Analysis**: Extract insights from academic abstracts and papers

### Educational Applications

* **Curriculum Development**: Understand current research trends for course planning
* **Research Guidance**: Help students identify research directions
* **Publication Analysis**: Analyze institutional research output
* **Knowledge Mapping**: Visualize research landscape and connections

## Key Features

### For Researchers

* **Easy-to-Use Interface**: No coding required for basic operations
* **Quick Topic Prediction**: Classify new research content instantly
* **Data Export**: Download results for further analysis
* **Flexible Input**: Support text input, file upload, and web scraping

### For System Administrators

* **Docker Deployment**: Containerized setup for easy deployment
* **Monitoring Integration**: Prometheus and Grafana for system health
* **API Access**: RESTful API for programmatic access
* **Background Processing**: Non-blocking operations for better user experience

## Technology Stack

PTIIKInsight uses modern, proven technologies:

* **Machine Learning**: BERTopic, sentence-transformers, scikit-learn
* **Backend API**: FastAPI with async support
* **Web Dashboard**: Streamlit with interactive components
* **Data Processing**: pandas, numpy for data manipulation
* **Web Scraping**: crawl4ai for automated data collection
* **Monitoring**: Prometheus metrics, Grafana dashboards
* **Containerization**: Docker and Docker Compose
* **Model Storage**: Pickle format for trained models

## System Requirements

**Minimum Requirements:**

* Python 3.8 or higher
* 4 GB RAM
* 5 GB storage space
* 2+ CPU cores

**Recommended for Production:**

* Python 3.10+
* 8 GB+ RAM
* 20 GB+ SSD storage
* 4+ CPU cores

## Getting Started

Ready to start using PTIIKInsight? Follow these steps:

1. [**Installation Guide**](/ptiikinsight/resources/installation.md) - Set up the platform (Docker or local)
2. [**Web UI Guide**](/ptiikinsight/resources/web-ui.md) - Learn to use the dashboard interface
3. [**API Reference**](/ptiikinsight/resources/api-reference.md) - Explore programmatic access
4. [**Docker Architecture**](/ptiikinsight/resources/docker-architecture.md) - Understand the system design

## Quick Start

### Using Docker (Recommended)

```bash
git clone https://github.com/your-org/PTIIKInsight.git
cd PTIIKInsight/project
docker-compose up -d
```

### Local Installation

```bash
cd project
pip install -r requirements.txt
pip install -r dashboard/requirements.txt

# Start API
uvicorn api.main:app --reload &

# Start Dashboard  
streamlit run dashboard/main.py
```

Access the dashboard at: <http://localhost:8501>

## Support and Documentation

* **Installation Help**: [Installation Guide](/ptiikinsight/resources/installation.md)
* **Usage Instructions**: [Web UI Guide](/ptiikinsight/resources/web-ui.md)
* **API Documentation**: [API Reference](/ptiikinsight/resources/api-reference.md)
* **Troubleshooting**: [Troubleshooting Guide](/ptiikinsight/resources/troubleshooting.md)

***

*Continue to the* [*Installation Guide*](/ptiikinsight/resources/installation.md) *to get started with PTIIKInsight.*