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aryaputras
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README.md
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README.md
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# Nemesis
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# Nemesis
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Nemesis is the public-facing investigative interface as the result of Operation Diponegoro, initiated by Abil Sudarman School of Artificial Intelligence. We ingest millions of rows of procurement data, surface anomalies, and make the findings legible to citizens, journalists, and policymakers.
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assai.id/nemesis
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https://assai.id/nemesis
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> End the vampire ball.
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> End the vampire ball.
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Mohon bersabar, file-file sedang dalam proses finalisasi.
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## Release Status
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## Release Status
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| Asset | Status | ETA |
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| Asset | Status | ETA |
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|-------|--------|-----|
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|-------|--------|-----|
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| Dashboard URL | 🟡 In progress | 17 April |
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| Dashboard URL | 🟡 In progress | 17 April |
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| Fine-tuned model | 🟡 In progress | ? |
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| SQL / SQLite / Raw Dataset Files | 🟡 In progress | 18 April |
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Stay tuned.
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Stay tuned.
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## Downloads
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## Downloads
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### Dataset
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### Raw Dataset (Analyzed by GPT-5.4)
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[Download SIRUP raw jsonl dataset (analyzed by GPT-5.4)](https://contenflowstorage.blob.core.windows.net/shared/gpt-5.4-analyzed-sirup.zip?sp=r&st=2026-04-16T12:00:08Z&se=2029-04-16T20:15:08Z&spr=https&sv=2025-11-05&sr=b&sig=m%2FATynnnZq5gSdP8xWWw2ew41EMJZz09fDQRwpbWolk%3D)
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[Download SIRUP dataset (analyzed)](https://contenflowstorage.blob.core.windows.net/shared/gpt-5.4-analyzed-sirup.zip?sp=r&st=2026-04-16T12:00:08Z&se=2029-04-16T20:15:08Z&spr=https&sv=2025-11-05&sr=b&sig=m%2FATynnnZq5gSdP8xWWw2ew41EMJZz09fDQRwpbWolk%3D)
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[Download SIRUP dataset SQL Ver (analyzed by GPT-5.4-mini)](https://contenflowstorage.blob.core.windows.net/shared/datasets/dashboard.sql?sp=r&st=2026-04-16T12:16:15Z&se=2029-04-16T20:31:15Z&spr=https&sv=2025-11-05&sr=b&sig=sKPH9uazyLcYcSwhARcEwVSG%2FTld9VnGJgZ2mOZIxrA%3D)
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### Model
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Stay tuned
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### 1. Prepare the Database
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### 1. Prepare the Database
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Download and unzip the raw dataset, then convert it to SQLite:
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Download and unzip the raw dataset, then convert it to SQLite:
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```bash
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```bash
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# Convert raw jsonl data / sql data
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# Unzip the dataset
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unzip gpt-5.4-analyzed-sirup.zip
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# Place the resulting .SQL file at the expected path:
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# Convert to SQLite (adjust based on source format)
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# Place the resulting .sqlite file at the expected path:
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mv dashboard.sqlite backend/data/dashboard.sqlite
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mv dashboard.sqlite backend/data/dashboard.sqlite
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```
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```
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The backend expects the database at `backend/data/dashboard.sqlite`.
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The backend expects the database at `backend/data/dashboard.sqlite`.
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### 2. Run the Application
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### 2. Run the Application
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**1. Start the backend**
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> ⚠️ Note: This is a time-limited SAS URL that expires on 16 April 2026 at 20:15 UTC. Request a refreshed link if access has lapsed.
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```bash
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cd backend
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npm start
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```
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The backend runs on `http://127.0.0.1:3000`.
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**2. Serve the frontend** (in a second terminal)
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```bash
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cd frontend
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python3 -m http.server 8080
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```
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Then open `http://127.0.0.1:8080` in your browser.
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The frontend is preconfigured to call the backend at `http://127.0.0.1:3000/api`.
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## Notes
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- No frontend build step required — plain HTML/CSS/JS.
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- If `backend/node_modules` is missing, install dependencies:
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```bash
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cd backend
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npm install
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```
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- For development mode with hot reload:
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```bash
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cd backend
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npm run dev
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```
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## Environment
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Backend config is loaded from `backend/.env`. Default values:
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```env
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PORT=3000
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CORS_ORIGIN=*
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SQLITE_PATH=data/dashboard.sqlite
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AUDIT_DATASET_DIR=dataset
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GEO_ROOT_PATH=seed/geo
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```
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