This repository contains a data science project that analyzes public sentiment regarding the 2025 revision of the TNI Law (RUU TNI), based on Twitter data collected during the discussion and ratification period.
To collect, clean, and analyze social media opinions related to the Revisi Undang-Undang TNI (RUU TNI), with the goal of understanding public perception and identifying dominant narratives and keywords across sentiment categories (positive, neutral, negative).
| Module | Description |
|---|---|
📰 1. TWT Scraping Twitter/ |
Twitter scraping using keyword-based queries |
🧼 2. TWT Data Cleaning/ |
Preprocessing and cleaning of raw tweet data |
📊 3. TWT Analysis & Visualization/ |
Exploratory analysis, sentiment breakdown, and visualization of top keywords |
- Primary: Twitter (via scraping using relevant keywords such as
RUU TNI,dwifungsi,militer,sipil, etc.)
The 2025 Revisi UU TNI was passed on March 20, 2025, triggering widespread public criticism due to its closed and rushed process, as well as perceived threats to civil liberties. Key revised articles include:
- Pasal 3: Administrative updates
- Pasal 7: Expanded OMSP operations overseas & reduced DPR oversight
- Pasal 8: Greater military involvement in civilian space
- Pasal 47: More public positions open to active TNI officers
- Pasal 53: Raised retirement age limits
Revisions to Articles 8 and 47 were heavily criticized for weakening civil supremacy.
- 4472 tweets were analyzed.
- Sentiment distribution:
- 🔴 Negative: 75.8%
- ⚪ Neutral: 22.7%
- 🟢 Positive: 1.5%
- ✅ Positive: "TNI", "dwifungsi", "RUU", "sipil"
- ⚪ Neutral: "TNI", "dwifungsi", "baru", "jadi", "DPR", "demo"
- ❌ Negative: "Tolak", "RUU", "TNI", "Indonesia"
- Python (Pandas, Sastrawi, Sklearn, Matplotlib, etc.)
- Sentiment analysis with lexicon-based or model-based approach
- Tokenization and keyword frequency extraction
- Data visualization
| Stage | Description | Status |
|---|---|---|
| Twitter Scraping | Collect tweets on RUU TNI | ✅ Done |
| Data Cleaning | Clean and preprocess tweets | ✅ Done |
| Sentiment Analysis | Classify tweets into sentiment categories | ✅ Done |
| Visualization | Generate insights and graphs | ✅ Done |
| Documentation | Create media & report assets | ✅ Done |
- Farhan Adiwidya Pradana
- Yusuf Imantaka Bastari
- Muhammad Javier
- Deira Aisya Rifani
- Danar Fathurahman
This project is part of the Gamadata-1 initiative under Data Research Division of Universitas Gadjah Mada's Student Union, aimed at using data science for social awareness and advocacy, particularly in supporting democratic oversight and civic participation in policymaking. Post: https://www.instagram.com/p/DJbOf_Av-xe/?img_index=1