---
language:
- id
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
tags:
- education
- bse
- kemdikbud
- bloom-taxonomy
- indonesian
- context-extraction
pretty_name: Indo-Bloom BSE RAW Corpus
size_categories:
- 1K<n<10K
---

# 📚 Indo-Bloom BSE RAW Corpus

<div style="background-color: #e6f7ff; padding: 15px; border-left: 5px solid #1890ff; border-radius: 5px; margin-bottom: 20px;">
<strong>⚠️ RESEARCH ARTIFACT STATUS: RAW CORPUS (Stage 0)</strong><br><br>
This dataset serves as the <strong>raw material corpus</strong> for the Indo-Bloom research project at <strong>Universitas Negeri Malang (UM)</strong>.<br><br>
<strong>Current State:</strong> <code>Extracted & Cleaned Context from BSE Textbooks</code><br>
<strong>Next Stage:</strong> <code>QA Pair Generation (Stage 1) → Silver Corpus</code>
</div>

<div style="background-color: #fff3cd; padding: 12px; border-left: 5px solid #ffc107; border-radius: 5px; margin-bottom: 20px;">
<strong>🔒 FROZEN — Raw v1.0</strong><br>
This version is <strong>permanently frozen</strong> to ensure reproducibility. This corpus will be used as input for QA generation pipeline.
</div>

---

## 📄 Associated Publication

**Preprint Paper:**  
Ibrahim, F., Prasetya, D. D., & Widiyaningtyas, T. (2026). *Towards Indo-Bloom: A Preliminary Study on Controllable Bloom's Taxonomy-aligned Question Generation in Indonesian*. **TechRxiv**.

**DOI:** [`10.36227/techrxiv.177220024.48567886/v1`](https://doi.org/10.36227/techrxiv.177220024.48567886/v1)  
**Publication Date:** February 27, 2026  
**Status:** 📝 Preprint (Not Peer-Reviewed)

**Read the Full Paper:** [TechRxiv Preprint](https://doi.org/10.36227/techrxiv.177220024.48567886/v1)

---

## 🎓 Dissertation Details

- **Project Title:** *A Unified Framework for Controllable Indonesian Automatic Question Generation Aligned with Bloom's Taxonomy*
- **Principal Investigator:** Firmansyah Ibrahim (Ph.D. Candidate)
- **Student ID (NIM):** 250534903885
- **Institution:** Doctoral Program in Electrical and Informatics Engineering, Universitas Negeri Malang (UM)
- **Supervision Team:**
  - **Promotor:** Dr. Eng. Didik Dwi Prasetya, S.T., M.T.
  - **Co-Promotor:** Dr. Ir. Triyanna Widiyaningtyas, M.T.

---

## 📝 Dataset Description

**Indo-Bloom BSE RAW Corpus** is a curated collection of educational text chunks extracted from Indonesian high school textbooks (Buku Sekolah Elektronik - BSE) published by Kemdikbudristek. This corpus serves as the foundational raw material for generating Bloom's Taxonomy-aligned question-answer pairs in the Indo-Bloom research project.

### Purpose in Research Pipeline

This dataset represents **Stage 0** in the Indo-Bloom methodology:
```
Stage 0 (This Dataset) → Stage 1 (QA Generation) → Stage 2 (Expert Annotation) → Stage 3 (Gold Standard)
```

**Role:**
- Provides clean, domain-specific context passages for QA generation
- Ensures educational content quality and curriculum alignment
- Enables controlled generation of pedagogically valid questions

---

## 📊 Dataset Specifications

### 1. Corpus Statistics

| Metric | Value |
|:-------|------:|
| **Total Chunks** | 1,825 |
| **Total Words** | ~292,000 |
| **Avg Chunk Length** | 160 words |
| **Min Chunk Length** | 50 words |
| **Max Chunk Length** | 450 words |

### 2. Subject Distribution

| Mata Pelajaran | Chunks | Percentage | Description |
|:---------------|-------:|-----------:|:------------|
| **Sejarah** | ~450 | 24.7% | Indonesian History |
| **Geografi** | ~460 | 25.2% | Geography |
| **Biologi** | ~455 | 24.9% | Biology |
| **Sosiologi** | ~460 | 25.2% | Sociology |
| **TOTAL** | 1,825 | 100% | — |

### 3. Grade Level Distribution

| Jenjang | Kelas | Chunks | Percentage |
|:--------|:------|-------:|-----------:|
| **SMA/MA** | X (10) | ~610 | 33.4% |
| **SMA/MA** | XI (11) | ~607 | 33.3% |
| **SMA/MA** | XII (12) | ~608 | 33.3% |
| **TOTAL** | — | 1,825 | 100% |

---

## 📂 Data Structure

Each row in the dataset contains the following fields:

| Field | Type | Description | Example |
|:------|:-----|:------------|:--------|
| `chunk_id` | `string` | Unique identifier | `chunk_0001` |
| `mata_pelajaran` | `string` | Subject name | `Sejarah` |
| `jenjang` | `string` | Grade level | `SMA/MA` |
| `kelas` | `string` | Specific grade | `X`, `XI`, `XII` |
| `context` | `string` | Text passage (50-450 words) | *"Indonesia memiliki sejarah panjang..."* |
| `word_count` | `integer` | Number of words in context | 160 |
| `noise_score` | `integer` | Quality metric (0 = clean) | 0 |
| `source_file` | `string` | Original BSE filename | `Sejarah_X_2024.pdf` |
| `page_range` | `string` | Source page numbers | `15-20` |

---

## 💻 How to Use

### Loading the Dataset (Python)
```python
from datasets import load_dataset

# Load the BSE RAW corpus
dataset = load_dataset("Firmansyah-Ibrahim/indo-bloom-bse-raw")

# Print first example
print(dataset['train'][0])

# Output structure:
# {
#   'chunk_id': 'chunk_0001',
#   'mata_pelajaran': 'Sejarah',
#   'jenjang': 'SMA/MA',
#   'kelas': 'X',
#   'context': '...',
#   'word_count': 160,
#   'noise_score': 0,
#   'source_file': 'Sejarah_X_2024.pdf',
#   'page_range': '15-20'
# }
```

### Filtering by Subject
```python
import pandas as pd

# Load as pandas DataFrame
df = pd.DataFrame(dataset['train'])

# Filter by subject
sejarah_chunks = df[df['mata_pelajaran'] == 'Sejarah']
geografi_chunks = df[df['mata_pelajaran'] == 'Geografi']
biologi_chunks = df[df['mata_pelajaran'] == 'Biologi']
sosiologi_chunks = df[df['mata_pelajaran'] == 'Sosiologi']

print(f"Sejarah: {len(sejarah_chunks)} chunks")
print(f"Geografi: {len(geografi_chunks)} chunks")
```

### Filtering by Grade Level
```python
# Filter by grade
kelas_10 = df[df['kelas'] == 'X']
kelas_11 = df[df['kelas'] == 'XI']
kelas_12 = df[df['kelas'] == 'XII']

print(f"Kelas X: {len(kelas_10)} chunks")
print(f"Kelas XI: {len(kelas_11)} chunks")
print(f"Kelas XII: {len(kelas_12)} chunks")
```

### Quality Filtering
```python
# Get only high-quality chunks (noise_score = 0)
clean_chunks = df[df['noise_score'] == 0]

# Filter by word count range
medium_chunks = df[(df['word_count'] >= 100) & (df['word_count'] <= 200)]

print(f"Clean chunks: {len(clean_chunks)}")
print(f"Medium-length chunks: {len(medium_chunks)}")
```

---

## 🔧 Data Processing Pipeline

### Extraction Process (IBEX v3.0)
```mermaid
graph TD
    A[BSE PDF Files] -->|IBEX Extractor| B[Raw Text Extraction]
    B -->|Noise Filter L1| C[Remove Instructional Content]
    C -->|Noise Filter L2| D[Remove Exercise/Quiz Patterns]
    D -->|Sentence Cleaning| E[Remove Pilgan Patterns]
    E -->|Chunking Algorithm| F[Fixed-size Chunks 150±50 words]
    F -->|Quality Check| G[Noise Score = 0]
    G -->|Metadata Tagging| H[Final BSE RAW Corpus]
```

### Technical Specifications

#### Extraction Tool
- **Tool:** IBEX v3.0 (Indo-Bloom Context Extractor)
- **PDF Library:** PyMuPDF (fitz)
- **Processing:** Python 3.8+

#### Cleaning Filters

**Level 1 (Noise Patterns):**
- Instructional phrases: "Simak gambar", "Jawablah pertanyaan"
- Learning objectives: "Tujuan pembelajaran", "Kata kunci"
- Metadata: ISBN, author names, page numbers

**Level 2 (Sentence Cleaning):**
- Multiple-choice patterns: `a. ... b. ... c. ...`
- Question prompts: "Diskusikan dengan teman"
- Activity instructions: "Buatlah laporan"

#### Chunking Algorithm
```python
# Chunking strategy
CHUNK_SIZE = 150  # words
OVERLAP = 37      # 25% overlap
MIN_LENGTH = 50   # minimum viable chunk
MAX_LENGTH = 450  # maximum to prevent context overflow

# Quality threshold
NOISE_THRESHOLD = 0  # Only chunks with score 0 included
```

---

## 🔄 Dataset Relationship in Research Pipeline

### Stage 0: BSE RAW Corpus (This Dataset)

**Input:** PDF textbooks from Kemdikbudristek  
**Process:** IBEX v3.0 extraction & cleaning  
**Output:** 1,825 clean educational text chunks  
**Status:** ✅ Completed & Frozen

### Stage 1: QA Generation

**Input:** BSE RAW Corpus (this dataset)  
**Process:** Qwen2.5-3B-Instruct with Kemdikbud prompts  
**Output:** [Indo-Bloom Silver Corpus](https://huggingface.co/datasets/Firmansyah-Ibrahim/indo-bloom-corpus) (2,768 QA pairs)  
**Status:** ✅ Completed

### Stage 2: Expert Annotation

**Input:** Indo-Bloom Silver Corpus  
**Process:** Human expert validation (3 annotators)  
**Output:** Indo-Bloom Annotated Corpus  
**Status:** 🔵 In Progress

### Stage 3: Gold Standard

**Input:** Indo-Bloom Annotated Corpus  
**Process:** IAA validation (Kappa ≥ 0.70)  
**Output:** Indo-Bloom Gold Corpus v1.0  
**Status:** 🟡 Planned

---

## 📋 Extraction Scripts Documentation

### Script: IBEX v3.0 (Indo-Bloom Context Extractor)

**Input:** BSE PDF files  
**Output:** CSV with cleaned text chunks

**Key Features:**
- Margin removal (8% top/bottom)
- Hyphenation correction
- Noise pattern detection (30+ patterns)
- Chunk size balancing with overlap
- Domain metadata preservation

**Extraction Statistics:**

| Metric | Value |
|:-------|------:|
| **Raw pages processed** | ~2,500 |
| **Raw chunks extracted** | ~3,200 |
| **After noise filtering** | 1,825 (57% retention) |
| **Avg processing time** | ~2.5 min/book |

---

## 🚀 Research Roadmap

- [x] **Stage 0 (Completed):** BSE text extraction & cleaning (This Dataset)
- [x] **Stage 1 (Completed):** QA generation with LLM (Silver Corpus)
- [ ] **Stage 2 (In Progress):** Expert annotation
- [ ] **Stage 3 (Planned):** IAA validation & Gold Standard release
- [ ] **Stage 4 (Future):** Model training & evaluation

---

## ⚖️ License & Attribution

### Dataset License

**License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

You are free to:
- ✅ Share — copy and redistribute the material
- ✅ Adapt — remix, transform, and build upon the material

Under the following terms:
- **Attribution** — You must give appropriate credit

### Source Material

**Original Content:** Buku Sekolah Elektronik (BSE)  
**Publisher:** Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Kemdikbudristek) Indonesia  
**Original License:** CC BY 4.0 (as per BSE repository)

**Books Included:**
- Sejarah Indonesia (Kelas X, XI, XII)
- Geografi Indonesia (Kelas X, XI, XII)
- Biologi (Kelas X, XI, XII)
- Sosiologi (Kelas X, XI, XII)

---

## 📜 Citation

If you use this dataset in your research, please cite:

### Primary Citation (Dataset Repository)
```bibtex
@misc{ibrahim2026bseraw,
  author       = {Ibrahim, Firmansyah},
  title        = {Indo-Bloom BSE RAW Corpus: Educational Context Extraction from Indonesian Textbooks},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Firmansyah-Ibrahim/indo-bloom-bse-raw}},
  note         = {Raw corpus for Indo-Bloom AQG research. Extracted from BSE Kemdikbudristek.}
}
```

### Secondary Citation (Associated Research Paper)
```bibtex
@article{ibrahim2026indobloom_paper,
  author    = {Ibrahim, Firmansyah and Prasetya, Didik Dwi and Widiyaningtyas, Triyanna},
  title     = {Towards Indo-Bloom: A Preliminary Study on Controllable Bloom's 
               Taxonomy-aligned Question Generation in Indonesian},
  journal   = {TechRxiv},
  year      = {2026},
  month     = {February},
  day       = {27},
  doi       = {10.36227/techrxiv.177220024.48567886/v1},
  url       = {https://doi.org/10.36227/techrxiv.177220024.48567886/v1},
  note      = {Preprint — not peer-reviewed}
}
```

### Combined Citation (For Academic Papers)

**Text Format:**
> Ibrahim, F. (2026). *Indo-Bloom BSE RAW Corpus: Educational Context Extraction from Indonesian Textbooks*. Hugging Face. https://huggingface.co/datasets/Firmansyah-Ibrahim/indo-bloom-bse-raw  
> Associated paper: Ibrahim, F., Prasetya, D. D., & Widiyaningtyas, T. (2026). *Towards Indo-Bloom*. TechRxiv. https://doi.org/10.36227/techrxiv.177220024.48567886/v1

---

## 📞 Contact & Support

**Principal Investigator:**  
Firmansyah Ibrahim (Ph.D. Candidate)  
📧 Email: firmansyah.ibrahim.2505349@students.um.ac.id  
🏛️ Institution: Universitas Negeri Malang  
🔗 Dataset: [indo-bloom-bse-raw](https://huggingface.co/datasets/Firmansyah-Ibrahim/indo-bloom-bse-raw)  
📄 Paper: [TechRxiv Preprint](https://doi.org/10.36227/techrxiv.177220024.48567886/v1)

**Supervision Team:**
- Dr. Eng. Didik Dwi Prasetya, S.T., M.T. — Promotor
- Dr. Ir. Triyanna Widiyaningtyas, M.T. — Co-Promotor

---

## 🙏 Acknowledgments

We gratefully acknowledge:

- **Kemdikbudristek Indonesia** for publishing BSE textbooks under CC BY 4.0
- **Universitas Negeri Malang** for institutional support
- **Hugging Face** for open-source hosting infrastructure
- **Indonesian NLP Community** for domain expertise
- **TechRxiv** (IEEE) for preprint hosting

---

## ⚠️ Disclaimer

This dataset is extracted from educational materials published by Kemdikbudristek Indonesia. While we have performed careful cleaning and quality control, users should:

- ✅ Verify extracted content against original sources for critical applications
- ✅ Respect the original CC BY 4.0 license terms
- ✅ Acknowledge both this corpus and the original BSE sources

**Educational Use:** This corpus is intended for research and educational purposes, particularly for developing AI-assisted educational tools aligned with the Indonesian national curriculum.

---

## 🔗 Related Datasets

| Dataset | Stage | Status | Link |
|:--------|:------|:------:|:-----|
| **BSE RAW Corpus** | Stage 0 | ✅ Released | [This dataset] |
| **Indo-Bloom Silver** | Stage 1 | ✅ Released | [indo-bloom-corpus](https://huggingface.co/datasets/Firmansyah-Ibrahim/indo-bloom-corpus) |
| **Indo-Bloom Annotated** | Stage 2 | 🔵 In Progress | Coming Soon |
| **Indo-Bloom Gold** | Stage 3 | 🟡 Planned | TBA |

---

**Last Updated:** February 28, 2026  
**Version:** Raw v1.0 — 🔒 FROZEN  
**Status:** ✅ Production Ready — Stage 0 Complete  
**Paper DOI:** [`10.36227/techrxiv.177220024.48567886/v1`](https://doi.org/10.36227/techrxiv.177220024.48567886/v1)

---

*© 2026 Firmansyah Ibrahim | Universitas Negeri Malang*