# 🇮🇩 Javanese Stemmer
A comprehensive Javanese language stemmer with advanced morphophonological rules support.
## ✨ Features
- ✅ Comprehensive morphological analysis
- ✅ Nasal assimilation handling (ny, ng, m, n)
- ✅ Prefix, suffix, infix, and confix support
- ✅ Passive and active voice transformation
- ✅ Person marking (1st, 2nd person)
- ✅ Phonological rule application
- ✅ High accuracy stemming
- ✅ Sastrawi-compatible interface
## 📦 Installation
```bash
pip install javanese-stemmer
```
## 🚀 Quick Start
### Basic Stemming
```python
from javanese_stemmer import stem_word
# Stem a single word
result = stem_word("mangan")
print(result) # Output: "pangan"
```
### Using the Stemmer Class
```python
from javanese_stemmer import JavaneseStemmerLibrary
# Initialize stemmer
stemmer = JavaneseStemmerLibrary()
# Stem words
print(stemmer.stem("mangan")) # "pangan"
print(stemmer.stem("dipangan")) # "pangan"
print(stemmer.stem("tetuku")) # "tuku"
print(stemmer.stem("nyapu")) # "sapu"
print(stemmer.stem("nggawa")) # "gawa"
```
### Stem Sentences
```python
from javanese_stemmer import JavaneseStemmerLibrary
stemmer = JavaneseStemmerLibrary()
sentence = "Aku mangan sega ing warung"
stemmed = stemmer.stem_kalimat(sentence)
print(stemmed)
# Output: ['aku', 'pangan', 'sega', 'ing', 'warung']
```
### Stem Text
```python
from javanese_stemmer import stem_text
text = "Dheweke lagi mangan panganan enak"
stemmed_text = stem_text(text)
print(stemmed_text)
```
### Detailed Morphological Analysis
```python
from javanese_stemmer import JavaneseStemmerLibrary
stemmer = JavaneseStemmerLibrary()
# Get detailed analysis
analysis = stemmer.stem_detailed("dipanganiake")
print(f"Original: {analysis['original']}")
print(f"Stem: {analysis['stem']}")
print(f"Morphemes: {analysis['morphemes']}")
print(f"Confidence: {analysis['confidence']}")
```
## 📖 Complete Examples
### Example 1: Basic Word Stemming
```python
from javanese_stemmer import stem_word
words = ["mangan", "dipangan", "panganan", "takpanganiake"]
for word in words:
print(f"{word:20} → {stem_word(word)}")
# Output:
# mangan → pangan
# dipangan → pangan
# panganan → pangan
# takpanganiake → pangan
```
### Example 2: Batch Processing
```python
from javanese_stemmer import JavaneseStemmerLibrary
stemmer = JavaneseStemmerLibrary()
sentences = [
"Aku mangan sega",
"Dheweke lagi turu",
"Bocah-bocah dolanan ing alun-alun"
]
for sentence in sentences:
stemmed = stemmer.stem_kalimat(sentence)
print(f"{sentence:40} → {' '.join(stemmed)}")
```
### Example 3: Document Processing
```python
from javanese_stemmer import JavaneseStemmerLibrary
stemmer = JavaneseStemmerLibrary()
document = """
Jawa iku basa sing sugih. Akeh wong nganggo basa Jawa
kanggo komunikasi saben dina. Basa Jawa nduweni tataran
krama lan ngoko.
"""
result = stemmer.process_document(document, detailed=True)
print(f"Total words: {result['document_stats']['total_words']}")
print(f"Unique stems: {result['document_stats']['unique_stems']}")
```
## 🔧 API Reference
### Main Functions
#### `stem_word(word: str) -> str`
Quickly stem a single word.
**Parameters:**
- `word` (str): Word to stem
**Returns:**
- str: Stemmed word
#### `stem_sentence(sentence: str) -> list`
Stem all words in a sentence.
**Parameters:**
- `sentence` (str): Sentence to stem
**Returns:**
- list: List of stemmed words
#### `stem_text(text: str) -> str`
Stem entire text while preserving structure.
**Parameters:**
- `text` (str): Text to stem
**Returns:**
- str: Text with all words stemmed
### Classes
#### `JavaneseStemmerLibrary`
Main stemmer class with Sastrawi-compatible interface.
**Methods:**
##### `stem(word: str) -> str`
Stem a single word.
##### `stem_kalimat(sentence: str) -> list`
Stem a sentence and return list of stemmed words.
##### `stem_text(text: str) -> str`
Stem text while preserving structure.
##### `stem_detailed(word: str) -> dict`
Get detailed morphological analysis.
**Returns:**
```python
{
'original': str,
'stem': str,
'morphemes': list,
'transformations': list,
'confidence': float
}
```
##### `process_document(text: str, detailed: bool = False) -> dict`
Process entire document with statistics.
**Returns:**
```python
{
'processed_sentences': list,
'document_stats': {
'total_words': int,
'unique_stems': int,
'total_sentences': int
}
}
```
#### `StemmerFactory`
Factory class for creating stemmer instances.
```python
from javanese_stemmer import StemmerFactory
stemmer = StemmerFactory.create_stemmer()
```
## 🎯 Supported Features
### Nasal Prefixes
- `ny-` (nyapu → sapu, nyuci → suci)
- `ng-` (nggawa → gawa, nggambar → gambar)
- `m-` (mangan → pangan, mbanyu → banyu)
- `n-` (nulis → tulis, nduweni → duweni)
- `nge-` (ngelak → elak)
### Passive Prefixes
- `di-` (dipangan → pangan)
- `dipun-` (dipunpangan → pangan, formal)
- `ka-` (kapangan → pangan, archaic)
- `ke-` (kepangan → pangan, accidental)
### Active Prefixes (Person Marking)
- `tak-` (1st person informal: takpangan → pangan)
- `dak-` (1st person very informal: dakpangan → pangan)
- `kok-` (2nd person informal: kokpangan → pangan)
### Other Prefixes
- `pa-` (pasugihan → sugih)
- `pi-` (piandel → andel)
- `pra-` (pramugari → mugari)
- `sa-` (saperlu → perlu)
- `tar-` (taruban → uban)
### Suffixes
- `-ake` (panganiake → pangan)
- `-e` (pangane → pangan)
- `-i` (pangani → pangan)
- `-an` (panganan → pangan)
- `-en` (panganen → pangan)
- `-na` (panganna → pangan)
### Confixes (Prefix + Suffix combinations)
- `pa-...-an` (pasugihan → sugih)
- `ka-...-an` (kabahagiaan → bahagia)
- `di-...-ake` (dipanganiake → pangan)
- `tak-...-ake` (takpanganiake → pangan)
- `ke-...-an` (kepanasan → panas)
- `sa-...-e` (sagedhene → gedhe)
### Infixes
- `-in-` (tinulis → tulis)
- `-um-` (kumaput → kaput)
## 🧪 Technical Details
This stemmer implements:
- **Comprehensive morphophonological rules** for Javanese
- **Nasal assimilation patterns** (m, n, ny, ng)
- **Vowel harmony** considerations
- **Consonant cluster** handling
- **Etymology tracking**
- **Confidence scoring** for stem accuracy
- **Recursive affix** removal
- **Irregular word** handling
## 💻 Requirements
- Python >= 3.7
- pandas >= 1.0.0
## 📝 License
MIT License
Copyright (c) 2025 Stevia Anlena Putri
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 📧 Contact
For questions and feedback:
- Email: your.email@example.com
- GitHub: https://github.com/yourusername/javanese-stemmer
## 🙏 Acknowledgments
Special thanks to the Javanese linguistics community and all contributors to Javanese NLP research.
## 📚 Citation
If you use this stemmer in your research, please cite:
```bibtex
@software{javanese_stemmer,
title={Javanese Stemmer: A Comprehensive Morphological Analyzer},
author={Stevia Anlena Putri},
year={2025},
url={https://github.com/yourusername/javanese-stemmer}
}
```
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"description": "# \ud83c\uddee\ud83c\udde9 Javanese Stemmer\r\n\r\nA comprehensive Javanese language stemmer with advanced morphophonological rules support.\r\n\r\n## \u2728 Features\r\n\r\n- \u2705 Comprehensive morphological analysis\r\n- \u2705 Nasal assimilation handling (ny, ng, m, n)\r\n- \u2705 Prefix, suffix, infix, and confix support\r\n- \u2705 Passive and active voice transformation\r\n- \u2705 Person marking (1st, 2nd person)\r\n- \u2705 Phonological rule application\r\n- \u2705 High accuracy stemming\r\n- \u2705 Sastrawi-compatible interface\r\n\r\n## \ud83d\udce6 Installation\r\n\r\n```bash\r\npip install javanese-stemmer\r\n```\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Basic Stemming\r\n\r\n```python\r\nfrom javanese_stemmer import stem_word\r\n\r\n# Stem a single word\r\nresult = stem_word(\"mangan\")\r\nprint(result) # Output: \"pangan\"\r\n```\r\n\r\n### Using the Stemmer Class\r\n\r\n```python\r\nfrom javanese_stemmer import JavaneseStemmerLibrary\r\n\r\n# Initialize stemmer\r\nstemmer = JavaneseStemmerLibrary()\r\n\r\n# Stem words\r\nprint(stemmer.stem(\"mangan\")) # \"pangan\"\r\nprint(stemmer.stem(\"dipangan\")) # \"pangan\"\r\nprint(stemmer.stem(\"tetuku\")) # \"tuku\"\r\nprint(stemmer.stem(\"nyapu\")) # \"sapu\"\r\nprint(stemmer.stem(\"nggawa\")) # \"gawa\"\r\n```\r\n\r\n### Stem Sentences\r\n\r\n```python\r\nfrom javanese_stemmer import JavaneseStemmerLibrary\r\n\r\nstemmer = JavaneseStemmerLibrary()\r\n\r\nsentence = \"Aku mangan sega ing warung\"\r\nstemmed = stemmer.stem_kalimat(sentence)\r\nprint(stemmed)\r\n# Output: ['aku', 'pangan', 'sega', 'ing', 'warung']\r\n```\r\n\r\n### Stem Text\r\n\r\n```python\r\nfrom javanese_stemmer import stem_text\r\n\r\ntext = \"Dheweke lagi mangan panganan enak\"\r\nstemmed_text = stem_text(text)\r\nprint(stemmed_text)\r\n```\r\n\r\n### Detailed Morphological Analysis\r\n\r\n```python\r\nfrom javanese_stemmer import JavaneseStemmerLibrary\r\n\r\nstemmer = JavaneseStemmerLibrary()\r\n\r\n# Get detailed analysis\r\nanalysis = stemmer.stem_detailed(\"dipanganiake\")\r\nprint(f\"Original: {analysis['original']}\")\r\nprint(f\"Stem: {analysis['stem']}\")\r\nprint(f\"Morphemes: {analysis['morphemes']}\")\r\nprint(f\"Confidence: {analysis['confidence']}\")\r\n```\r\n\r\n## \ud83d\udcd6 Complete Examples\r\n\r\n### Example 1: Basic Word Stemming\r\n\r\n```python\r\nfrom javanese_stemmer import stem_word\r\n\r\nwords = [\"mangan\", \"dipangan\", \"panganan\", \"takpanganiake\"]\r\nfor word in words:\r\n print(f\"{word:20} \u2192 {stem_word(word)}\")\r\n\r\n# Output:\r\n# mangan \u2192 pangan\r\n# dipangan \u2192 pangan\r\n# panganan \u2192 pangan\r\n# takpanganiake \u2192 pangan\r\n```\r\n\r\n### Example 2: Batch Processing\r\n\r\n```python\r\nfrom javanese_stemmer import JavaneseStemmerLibrary\r\n\r\nstemmer = JavaneseStemmerLibrary()\r\n\r\nsentences = [\r\n \"Aku mangan sega\",\r\n \"Dheweke lagi turu\",\r\n \"Bocah-bocah dolanan ing alun-alun\"\r\n]\r\n\r\nfor sentence in sentences:\r\n stemmed = stemmer.stem_kalimat(sentence)\r\n print(f\"{sentence:40} \u2192 {' '.join(stemmed)}\")\r\n```\r\n\r\n### Example 3: Document Processing\r\n\r\n```python\r\nfrom javanese_stemmer import JavaneseStemmerLibrary\r\n\r\nstemmer = JavaneseStemmerLibrary()\r\n\r\ndocument = \"\"\"\r\nJawa iku basa sing sugih. Akeh wong nganggo basa Jawa\r\nkanggo komunikasi saben dina. Basa Jawa nduweni tataran\r\nkrama lan ngoko.\r\n\"\"\"\r\n\r\nresult = stemmer.process_document(document, detailed=True)\r\nprint(f\"Total words: {result['document_stats']['total_words']}\")\r\nprint(f\"Unique stems: {result['document_stats']['unique_stems']}\")\r\n```\r\n\r\n## \ud83d\udd27 API Reference\r\n\r\n### Main Functions\r\n\r\n#### `stem_word(word: str) -> str`\r\nQuickly stem a single word.\r\n\r\n**Parameters:**\r\n- `word` (str): Word to stem\r\n\r\n**Returns:**\r\n- str: Stemmed word\r\n\r\n#### `stem_sentence(sentence: str) -> list`\r\nStem all words in a sentence.\r\n\r\n**Parameters:**\r\n- `sentence` (str): Sentence to stem\r\n\r\n**Returns:**\r\n- list: List of stemmed words\r\n\r\n#### `stem_text(text: str) -> str`\r\nStem entire text while preserving structure.\r\n\r\n**Parameters:**\r\n- `text` (str): Text to stem\r\n\r\n**Returns:**\r\n- str: Text with all words stemmed\r\n\r\n### Classes\r\n\r\n#### `JavaneseStemmerLibrary`\r\nMain stemmer class with Sastrawi-compatible interface.\r\n\r\n**Methods:**\r\n\r\n##### `stem(word: str) -> str`\r\nStem a single word.\r\n\r\n##### `stem_kalimat(sentence: str) -> list`\r\nStem a sentence and return list of stemmed words.\r\n\r\n##### `stem_text(text: str) -> str`\r\nStem text while preserving structure.\r\n\r\n##### `stem_detailed(word: str) -> dict`\r\nGet detailed morphological analysis.\r\n\r\n**Returns:**\r\n```python\r\n{\r\n 'original': str,\r\n 'stem': str,\r\n 'morphemes': list,\r\n 'transformations': list,\r\n 'confidence': float\r\n}\r\n```\r\n\r\n##### `process_document(text: str, detailed: bool = False) -> dict`\r\nProcess entire document with statistics.\r\n\r\n**Returns:**\r\n```python\r\n{\r\n 'processed_sentences': list,\r\n 'document_stats': {\r\n 'total_words': int,\r\n 'unique_stems': int,\r\n 'total_sentences': int\r\n }\r\n}\r\n```\r\n\r\n#### `StemmerFactory`\r\nFactory class for creating stemmer instances.\r\n\r\n```python\r\nfrom javanese_stemmer import StemmerFactory\r\n\r\nstemmer = StemmerFactory.create_stemmer()\r\n```\r\n\r\n## \ud83c\udfaf Supported Features\r\n\r\n### Nasal Prefixes\r\n- `ny-` (nyapu \u2192 sapu, nyuci \u2192 suci)\r\n- `ng-` (nggawa \u2192 gawa, nggambar \u2192 gambar)\r\n- `m-` (mangan \u2192 pangan, mbanyu \u2192 banyu)\r\n- `n-` (nulis \u2192 tulis, nduweni \u2192 duweni)\r\n- `nge-` (ngelak \u2192 elak)\r\n\r\n### Passive Prefixes\r\n- `di-` (dipangan \u2192 pangan)\r\n- `dipun-` (dipunpangan \u2192 pangan, formal)\r\n- `ka-` (kapangan \u2192 pangan, archaic)\r\n- `ke-` (kepangan \u2192 pangan, accidental)\r\n\r\n### Active Prefixes (Person Marking)\r\n- `tak-` (1st person informal: takpangan \u2192 pangan)\r\n- `dak-` (1st person very informal: dakpangan \u2192 pangan)\r\n- `kok-` (2nd person informal: kokpangan \u2192 pangan)\r\n\r\n### Other Prefixes\r\n- `pa-` (pasugihan \u2192 sugih)\r\n- `pi-` (piandel \u2192 andel)\r\n- `pra-` (pramugari \u2192 mugari)\r\n- `sa-` (saperlu \u2192 perlu)\r\n- `tar-` (taruban \u2192 uban)\r\n\r\n### Suffixes\r\n- `-ake` (panganiake \u2192 pangan)\r\n- `-e` (pangane \u2192 pangan)\r\n- `-i` (pangani \u2192 pangan)\r\n- `-an` (panganan \u2192 pangan)\r\n- `-en` (panganen \u2192 pangan)\r\n- `-na` (panganna \u2192 pangan)\r\n\r\n### Confixes (Prefix + Suffix combinations)\r\n- `pa-...-an` (pasugihan \u2192 sugih)\r\n- `ka-...-an` (kabahagiaan \u2192 bahagia)\r\n- `di-...-ake` (dipanganiake \u2192 pangan)\r\n- `tak-...-ake` (takpanganiake \u2192 pangan)\r\n- `ke-...-an` (kepanasan \u2192 panas)\r\n- `sa-...-e` (sagedhene \u2192 gedhe)\r\n\r\n### Infixes\r\n- `-in-` (tinulis \u2192 tulis)\r\n- `-um-` (kumaput \u2192 kaput)\r\n\r\n## \ud83e\uddea Technical Details\r\n\r\nThis stemmer implements:\r\n- **Comprehensive morphophonological rules** for Javanese\r\n- **Nasal assimilation patterns** (m, n, ny, ng)\r\n- **Vowel harmony** considerations\r\n- **Consonant cluster** handling\r\n- **Etymology tracking**\r\n- **Confidence scoring** for stem accuracy\r\n- **Recursive affix** removal\r\n- **Irregular word** handling\r\n\r\n## \ud83d\udcbb Requirements\r\n\r\n- Python >= 3.7\r\n- pandas >= 1.0.0\r\n\r\n## \ud83d\udcdd License\r\n\r\nMIT License\r\n\r\nCopyright (c) 2025 Stevia Anlena Putri\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nContributions are welcome! 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