Crate sastrawi

Expand description

§sastrawi-rs

High-performance Indonesian & Javanese stemmer for Rust — Zero-regex, zero-copy, FST-powered.

A fully modernized Rust 2024 implementation of stemming algorithms for Bahasa Indonesia and Bahasa Jawa. Based on the Nazief-Adriani / Enhanced Confix Stripping (ECS) algorithm for Indonesian, and a dedicated multi-strata engine (Ngoko, Krama Alus, Krama Inggil) for Javanese. Fork of iDevoid/rust-sastrawi, itself a Rust port of PHP Sastrawi by Andy Librian.

Note: The crate is published as sastrawi-rs on crates.io but imported as sastrawi in Rust code (hyphens become underscores per Rust convention).

§Quick Start

Add to Cargo.toml:

[dependencies]
sastrawi-rs = "0.5"

This crate provides two independent stemmers that share the same zero-copy, FST-based architecture:

Stemmer	Import Path	Dictionary	Language
Indonesian	`sastrawi::{Dictionary, Stemmer}`	~26k root words	Bahasa Indonesia
Javanese	`sastrawi::javanese::{JavaneseDictionary, JavaneseStemmer}`	~1.3k root words	Bahasa Jawa (Ngoko/Krama)

They are completely independent — changing one has zero effect on the other.

§🇮🇩 Indonesian Stemmer

§What’s New (vs. original rust-sastrawi)

Feature	Old	New
Engine	Regex-based rules	Zero-regex manual string slicing
Dictionary	HashMap on every call	FST (Finite State Transducer) with `OnceLock`
Allocation	Heap strings everywhere	`Cow<'a, str>` zero-copy API
Prefix rules	Basic me-/ber-	Full Nazief-Adriani: me-, pe-, ber-, ter-, se-, di-, ke-, ku-, kau-
`menge-`/`penge-`	❌	✅ Monosyllabic base words (mengebom→bom)
`nge-`	❌	✅ Informal/colloquial prefix (ngecat→cat)
Confix	❌	✅ ke-an, per-an, ber-an, se-nya simultaneous strip
Loanword suffixes	❌	✅ -isme, -isasi, -isir, -is
Hyphenated clitics	❌	✅ kuasa-Mu, allah-lah, nikmat-Ku
Stopword filter	❌	✅ `stem_sentence_filtered` + `is_stopword`
Backtracking	Partial	Full Longest-Root / Conservative Stemming
Edition	Rust 2018	Rust 2024

§Usage

use sastrawi::{Dictionary, Stemmer};

fn main() {
    let dict = Dictionary::new();
    let stemmer = Stemmer::new(&dict);

    let sentence = "Perekonomian Indonesia sedang dalam pertumbuhan yang membanggakan";
    for word in stemmer.stem_sentence(sentence) {
        print!("{} ", word); // ekonomi indonesia sedang dalam tumbuh yang bangga
    }
}

use sastrawi::{Dictionary, Stemmer};

let dict = Dictionary::new();
let stemmer = Stemmer::new(&dict);

assert_eq!(stemmer.stem_word("membangunkan").as_ref(), "bangun");
assert_eq!(stemmer.stem_word("keberuntunganmu").as_ref(), "untung");
assert_eq!(stemmer.stem_word("mengebom").as_ref(), "bom");       // menge-
assert_eq!(stemmer.stem_word("ngecat").as_ref(), "cat");          // nge- informal
assert_eq!(stemmer.stem_word("keamanan").as_ref(), "aman");       // ke-an confix
assert_eq!(stemmer.stem_word("pertanian").as_ref(), "tani");      // per-an confix
assert_eq!(stemmer.stem_word("idealisasi").as_ref(), "ideal");    // -isasi loanword
assert_eq!(stemmer.stem_word("kuasa-Mu").as_ref(), "kuasa");      // hyphen clitic

§Stopword Filtering

Common function words (yang, di, dari, dalam, dengan, …) carry no semantic value for indexing or NLP analysis. stem_sentence_filtered removes them automatically:

use sastrawi::{Dictionary, Stemmer};

let dict = Dictionary::new();
let stemmer = Stemmer::new(&dict);

let sentence = "Perekonomian Indonesia sedang dalam pertumbuhan yang membanggakan";

// Without filter — all tokens included
let all: Vec<_> = stemmer.stem_sentence(sentence).collect();
// ["ekonomi", "indonesia", "sedang", "dalam", "tumbuh", "yang", "bangga"]

// With stopword filter — function words removed
let filtered: Vec<_> = stemmer.stem_sentence_filtered(sentence).collect();
// ["ekonomi", "indonesia", "tumbuh", "bangga"]

assert!(stemmer.is_stopword("yang"));     // true
assert!(!stemmer.is_stopword("ekonomi")); // false

§Custom Dictionary

use sastrawi::{Dictionary, Stemmer};

let words = &["aman", "tani", "bangun", "bom"];
let dict = Dictionary::custom(words);
let stemmer = Stemmer::new(&dict);

assert_eq!(stemmer.stem_word("keamanan").as_ref(), "aman");

§Indonesian API Reference

// Initialization
let dict = Dictionary::new();                   // bundled dictionary (~26k words)
let dict = Dictionary::custom(&["word", ...]); // custom word list
let stemmer = Stemmer::new(&dict);

// Stemming
stemmer.stem_word(word)               // → Cow<'_, str>  (zero-copy when unchanged)
stemmer.stem_sentence(sentence)       // → impl Iterator<Item = Cow<str>>
stemmer.stem_sentence_filtered(sent)  // → Iterator with stopwords removed

// Utilities
stemmer.is_stopword(word)             // → bool

§Indonesian Stemming Pipeline (Nazief-Adriani + ECS)

Input word
  │
  ├─ 0. Lowercase + hyphen-clitic strip  (kuasa-Mu → kuasa)
  ├─ 1. Dictionary lookup                → return if found
  ├─ 2. Remove Particle                  (-lah, -kah, -tah, -pun)
  ├─ 3. Remove Possessive                (-ku, -mu, -nya)
  ├─ 4. Remove Suffix + Prefix           (-kan/-an/-i + me-/pe-/ber-/ter-…)
  ├─ 5. ECS Confix                       (ke-an, per-an, ber-an simultaneously)
  ├─ 6. Prefix-only                      (Longest Root preference on original word)
  └─ 7. Pengembalian Akhir               (backtracking over suffix combinations)

§🫙 Javanese Stemmer (Bahasa Jawa) 🆕

v0.4.0 introduces a dedicated Universal Javanese stemmer based on adaptations of the Nazief-Adriani algorithm for Javanese [⁵][⁶]. It is fully isolated from the Indonesian stemmer — different dictionary, different pipeline, different module.

§Usage

use sastrawi::javanese::{JavaneseDictionary, JavaneseStemmer};

// Uses the bundled Javanese dictionary (~1.3k pure root words)
let jv_dict = JavaneseDictionary::new();
let jv_stemmer = JavaneseStemmer::new(&jv_dict);

// Ngoko — Anuswara Meluluhkan
assert_eq!(jv_stemmer.stem_word("mangan").as_ref(), "pangan");    // m- + pangan
assert_eq!(jv_stemmer.stem_word("nulis").as_ref(), "tulis");       // n- + tulis

// Ngoko — Anuswara Menempel
assert_eq!(jv_stemmer.stem_word("ndawuhi").as_ref(), "dawuh");     // n- attaches to d
assert_eq!(jv_stemmer.stem_word("mbalang").as_ref(), "balang");    // m- attaches to b

// Krama Passives + Causatives
assert_eq!(jv_stemmer.stem_word("dipunjupuk").as_ref(), "jupuk");
assert_eq!(jv_stemmer.stem_word("lampahaken").as_ref(), "lampah");

// Circumfix Backtracking
assert_eq!(jv_stemmer.stem_word("nglebetaken").as_ref(), "lebet"); // ng-…-aken

// Monosyllabic root (nge-)
assert_eq!(jv_stemmer.stem_word("ngecet").as_ref(), "cet");

§Custom Dictionary

use sastrawi::javanese::{JavaneseDictionary, JavaneseStemmer};

let roots = &["gawa", "jupuk", "tulis", "dawuh"];
let jv_dict = JavaneseDictionary::custom(roots);
let jv_stemmer = JavaneseStemmer::new(&jv_dict);

assert_eq!(jv_stemmer.stem_word("nggawa").as_ref(), "gawa");
assert_eq!(jv_stemmer.stem_word("ndawuhi").as_ref(), "dawuh");

§Javanese Affix Rules Reference

All rules are derived from academic paper adaptations of Nazief-Adriani for Javanese [⁵][⁶][⁷].

§1. Ater-ater Anuswara (Nasalization) [⁶]

The most complex aspect of Javanese morphology. Rules differ by whether the nasal replaces the initial consonant (Meluluhkan) or simply prepends to it (Menempel).

Prefix	Meluluhkan (replaces)	Menempel (attaches to)	Vowel-initial
`m-`	`p → m` (macul←pacul), `w → m` (maca←waca)	`b` (mbalang←balang)	✅ (munggah←unggah)
`n-`	`t → n` (nulis←tulis), `th → n` (nuthuk←thuthuk)	`d`, `dh`, `j` (ndawuh←dawuh, njupuk←jupuk)	✅
`ng-`	`k → ng` (ngirim←kirim)	`g` (ngguyu←guyu)	✅ (ngombe←ombe)
`ny-`	`s → ny` (nyapu←sapu), `c → ny` (nyekel←cekel)	—	—
`nge-`	Monosyllabic roots (ngecet←cet, ngecat←cat) [special]	—	—

§2. Ater-ater Tripurusa & General Prefixes

Category	Prefixes
Krama Passive	`dipun-`
Tripurusa Ngoko	`di-`, `dak-`, `tak-`, `kok-`, `ko-`
Nominal Derivation [⁷]	`pan-`, `pam-`, `pang-` (allomorphs before labial/velar)
General	`pa-`, `pi-`, `ka-`, `sa-`, `ma-`, `ke-`, `pra-`
Formal/Literary [⁷]	`kuma-`, `kapi-`, `we-`, `a-`, `ben-`
Dialectal (Jawa Timur) [⁷]	`tar-`, `tok-`

§3. Panambang (Suffixes) & Allomorphs

Category	Suffixes
Particles	`-a`, `-i`, `-e`, `-en`, `-an`, `-na`, `-no` (Dialect)
Causative	`-ake` (Ngoko), `-aken` (Krama)
Possessives	`-ku`, `-mu`, `-ne`, `-ane` (allomorph), `-ipun` (Krama)
Vowel Sandhi Allomorphs [⁶]	`-kake`, `-kaken` (gunakake←guna), `-ni` (larani←lara), `-nan`
Complex suffixes	`-ana`, `-nan`

§4. Circumfix Backtracking (Confiks)

The pipeline performs exhaustive suffix-then-prefix stripping, meaning all circumfix combinations are resolved automatically without hardcoded confix rules. Examples:

dipunlampahaken → lampah   (dipun- … -aken)
nggunakake      → guna     (ng- … -kake allomorph)
nglebetaken     → lebet    (ng- … -aken)

§Javanese API Reference

// Initialization
let jv_dict = JavaneseDictionary::new();                 // bundled (~1.3k root words)
let jv_dict = JavaneseDictionary::custom(&["w1", ...]); // custom list (for testing)
let jv_stemmer = JavaneseStemmer::new(&jv_dict);

// Identical API surface as Indonesian stemmer
jv_stemmer.stem_word(word)          // → Cow<'_, str>
jv_stemmer.stem_sentence(sentence)  // → impl Iterator<Item = Cow<str>>

§Javanese Stemming Pipeline

Input word
  │
  ├─ 0. Lowercase + hyphen strip (ngisin-isini → isin via first segment)
  ├─ 1. Dictionary lookup        → return if found
  ├─ 2. Min length guard (< 3)   → return as-is
  ├─ 3. Exhaustive Suffix scan   → try ALL possessives × ALL particles
  │      └─ Dictionary check at each combination → return if found
  ├─ 4. Prefix removal on each suffix-stripped candidate
  │      └─ Anuswara (m-/n-/ng-/ny-/nge-) + Standard (di-/dipun-/pan-/…)
  └─ 5. Backtracking (Pengembalian Akhir) with known suffix combinations

Note on Infixes (Seselan): Javanese infixes -um-, -in-, -el-, -er- are intentionally not implemented in v0.4.0 as they require character-level mid-word insertion detection that conflicts with the zero-regex philosophy. Planned for v0.5.0 with an Aho-Corasick approach.

§🔬 MorphAnalyzer — Dictionary-Free Morphological Analyzer

MorphAnalyzer is a zero-dependency, no-dictionary morphological analyzer for Indonesian. It detects affix patterns and returns candidate roots without validating them against any dictionary.

Honest caveat: Because there is no dictionary, MorphAnalyzer cannot resolve all morphophonemic mutations (e.g. men- + tulis = menulis, but stripping men- yields ulis not tulis without knowing the root starts with t). It is accurate for affix detection and candidate generation, but not as a standalone stemmer.

§When to use `MorphAnalyzer` vs `Stemmer`

Need	Use
Single validated root from a word	`Stemmer` (requires dictionary)
Does this word have any affix?	`MorphAnalyzer`
What prefix/suffix does this word have?	`MorphAnalyzer`
Generate candidate roots for autocomplete or admin UI	`MorphAnalyzer`
Validate that a game submission is morphologically plausible	`MorphAnalyzer`
ML feature: prefix/suffix signals	`MorphAnalyzer`

§Usage

use sastrawi::{MorphAnalyzer, MorphAnalysis};

let ma = MorphAnalyzer::new(); // Zero allocation — no dictionary loaded

// --- Affix detection ---
let r = ma.analyze("membangunkan");
assert!(r.has_affix);
assert_eq!(r.prefix.as_deref(), Some("me"));
assert_eq!(r.suffix.as_deref(), Some("kan"));
// candidate_roots may contain ["mbangun"] — final resolution needs Stemmer+dictionary

// --- Plain word (no affix) ---
let r = ma.analyze("buku");
assert!(!r.has_affix);
assert!(r.candidate_roots.is_empty());

// --- Confix (ke-an, per-an, ber-an) ---
let r = ma.analyze("keamanan");
assert_eq!(r.prefix.as_deref(), Some("ke"));
assert_eq!(r.suffix.as_deref(), Some("an"));
assert!(r.candidate_roots.contains(&"aman".to_string()));

// --- nge- informal prefix ---
let r = ma.analyze("ngecat");
assert_eq!(r.prefix.as_deref(), Some("nge"));
assert!(r.candidate_roots.contains(&"cat".to_string()));

// --- Possessive suffix ---
let r = ma.analyze("rumahnya");
assert_eq!(r.suffix.as_deref(), Some("nya"));
assert!(r.candidate_roots.contains(&"rumah".to_string()));

// --- Hyphen-clitic stripped before analysis ---
let r = ma.analyze("kuasa-Mu");
assert_eq!(r.word, "kuasa"); // clitic segment after hyphen is dropped

§`MorphAnalysis` struct

pub struct MorphAnalysis {
    pub word: String,                  // normalized (lowercased, hyphen-stripped) input
    pub prefix: Option<String>,        // detected prefix ("me", "ber", "nge", "ke", …)
    pub suffix: Option<String>,        // detected suffix ("kan", "an", "lah", "ku", …)
    pub candidate_roots: Vec<String>,  // plausible roots (may be ambiguous — see caveats)
    pub has_affix: bool,               // true if prefix OR suffix detected
}

§Detection pipeline (priority order)

Input
  │
  ├─ 0. Lowercase + hyphen-clitic strip
  ├─ 1. Guard: word < 4 chars → return as-is (no analysis)
  ├─ 2. CONFIX (ke-an, per-an, ber-an, me-kan)  ← highest precision, both sides locked
  ├─ 3. PREFIX (me-/ber-/ter-/pe-/nge-/di-/se-/ke-/ku-/kau-)
  ├─ 4. PARTICLE (-lah, -kah, -tah, -pun), then prefix on remainder
  ├─ 5. POSSESSIVE (-ku, -mu, -nya), then prefix on remainder
  └─ 6. DERIVATIONAL SUFFIX (-kan, -an, -isme, -isasi, -isir)
         Guard: skipped if result < 3 chars, or -i suffix on short words

§Known limitations (by design)

Limitation	Reason	Workaround
`menulis` → root `ulis`, not `tulis`	`men-` drops nasal; `t` restoration needs dict	Use `Stemmer` for final root
`mengirim` → root `irim`, not `kirim`	`meng-` drops `k` which needs dict to restore	Use `Stemmer` for final root
Ambiguous: `mengada` → `["ada", "ngada"]`	Both morphologically valid without dict	Check candidates against dict
`-is` / `-i` suffix not stripped aggressively	Avoids false positives on short words	Intentional guard

§MorphAnalyzer API

let ma = MorphAnalyzer::new();    // or MorphAnalyzer::default()
let r: MorphAnalysis = ma.analyze(word);  // works on any &str

§🏗 Architecture

sastrawi-rs/
├── src/
│   ├── lib.rs                # Public API re-exports (both stemmers)
│   ├── stemmer.rs            # Indonesian: Nazief-Adriani pipeline + backtracking
│   ├── affixation.rs         # Indonesian: Prefix/suffix/confix orchestration
│   ├── affix_rules.rs        # Indonesian: Zero-regex morphological rules
│   ├── dictionary.rs         # Indonesian: FST-based dictionary (OnceLock)
│   ├── tokenizer.rs          # Shared zero-copy &str tokenizer
│   ├── stopword.rs           # Indonesian: FST stopword filter
│   └── javanese/
│       ├── mod.rs            # Javanese module re-exports
│       ├── stemmer.rs        # Javanese: Exhaustive suffix×prefix pipeline
│       ├── affixation.rs     # Javanese: Anuswara + Standard prefix orchestration
│       ├── affix_rules.rs    # Javanese: Meluluhkan/Menempel morphological rules
│       └── dictionary.rs     # Javanese: FST-based dictionary (OnceLock)
├── data/
│   ├── words.txt             # ~26k Indonesian root words (Kateglo, CC-BY-NC-SA 3.0)
│   ├── stopwords.txt         # Common Indonesian stopwords
│   └── javanese_words.txt    # ~1.3k Javanese root words (Riza et al. 2018, CC-BY 4.0)
├── build.rs                  # Compiles word lists → FST at build time
├── tests/test.rs             # Indonesian integration tests (6 suites, 200+ cases)
└── tests/javanese_test.rs    # Javanese integration tests (6 suites, 60+ cases)

§📊 Performance

Operation	Old (regex)	New (zero-regex FST)
Dictionary lookup	O(n) HashMap	O(k) FST where k = key length
Prefix stripping	Regex compile + match	Direct string slice comparison
Memory	Regex DFA state machines	FST bytes + OnceLock

§🧪 Testing

cargo test --release
# 12 test suites total (6 Indonesian + 6 Javanese), 260+ word cases

§Indonesian Suites

Suite	Coverage
`test_stem_word`	160+ Nazief-Adriani morphological cases
`test_stem_sentence`	Full sentence pipeline
`test_nge_informal_prefix`	`ngecat`, `ngegas`, `ngelepas`
`test_ecs_confixes`	`keamanan`, `pertanian`, `berhadapan`
`test_loanword_suffixes`	`-isasi`, `-isir`, `-isme`, `-is`
`test_stopword_filter`	`stem_sentence_filtered`, `is_stopword`

§Javanese Suites

Suite	Coverage
`test_javanese_anuswara_nasalization`	Meluluhkan & Menempel for `m-`, `n-`, `ng-`, `ny-`
`test_javanese_tripurusa_and_general_prefixes`	`di-`, `dipun-`, `dak-`, `ko-`, `ka-`, `kuma-`, etc.
`test_javanese_suffixes`	`-ake`, `-aken`, `-an`, `-ni`, `-ipun`, `-ne`, `-no`, etc.
`test_javanese_confixes_and_backtracking`	`dipunlampahaken`, `kepanasan`, `dituruake`
`test_javanese_complex_sandhi_and_circumfixes`	Vowel allomorphs (`nglarani`, `nggunakake`, `nglampahi`)
`test_javanese_new_affix_rules`	`pan-/pam-/pang-`, `nge-`, `-ane`, `kapi-`, `tar-/tok-`, `we-`

§🆕 Indonesian Extensions (2020–2026 Research)

Based on recent Indonesian NLP research (ECS [¹], IndoMorph [²], Aksara v1.5 [³]):

§A. `nge-` Informal Prefix

Colloquial prefix, mirror of menge-. Common in Jakarta informal speech and social media (e.g. MPStemmer [⁴]).

ngecat    → cat
ngegas    → gas
ngelamar  → lamar
ngelepas  → lepas

§B. Confixes — ECS (Enhanced Confix Stripping)

Simultaneous prefix+suffix removal, proven to outperform plain Nazief-Adriani in accuracy [¹].

keamanan    → aman    (ke-…-an)
pertanian   → tani    (per-…-an)
berhadapan  → hadap   (ber-…-an)

§C. Superlative `se-nya` Particle

selengkapnya  → lengkap
seberhasilnya → hasil

§D. Loanword Suffixes

idealisasi → ideal   (-isasi)
legalisir  → legal   (-isir)
idealisme  → ideal   (-isme)
idealis    → ideal   (-is)

§📚 References & Credits

§Indonesian Stemmer

[¹] ECS: Arifin, A., Mahendra, P., & Ciptaningtyas, H. T. (2009). Enhanced Confix Stripping Stemmer and Ants Algorithm for Classifying News Document in Indonesian Language.
[²] IndoMorph: Kamajaya, I., & Moeljadi, D. (2025). IndoMorph: a Morphology Engine for Indonesian. ACL Anthology.
[³] Aksara: Universitas Indonesia (2023). Aksara v1.5: Indonesian NLP tool conforming to UD v2 guidelines. GitHub.
[⁴] MPStemmer: Prabono, A. G. (2020). Mpstemmer: a multi-phase stemmer for standard and nonstandard Indonesian words. GitHub.
Algorithm: Nazief & Adriani (1996, 2007) — “Confix Stripping: Approach to Stemming Algorithm for Bahasa Indonesia”
PHP Sastrawi: Andy Librian — original PHP implementation
rust-sastrawi: iDevoid — original Rust port (2019)

§Javanese Stemmer

[⁵] Javanese Nazief-Adriani: Stemming Javanese: Another Adaptation of the Nazief-Adriani affix rules (2020) — ISRITI. Neliti.
[⁶] Javanese ECS: Ngoko Javanese Stemmer using Enhanced Confix Stripping — ResearchGate. ResearchGate.
[⁷] Complete Javanese Affix Taxonomy: Semantic Scholar (2021–2023) — A complete list of Javanese prefix/suffix rules including pan-/pam-/pang- nominal derivation, literary prefixes (kapi-, we-, a-), and dialectal forms (tar-, tok-). Semantic Scholar.
JV Dictionary: Riza, Hammam Riza et al. (2018) — Indonesian Javanese Dictionary Starter Kit Mendeley Data (CC-BY 4.0).

§General

sastrawi-rs: ibahasa Team — this modernized fork (2026)
ID Dictionary: Kateglo by Ivan Lanin (CC-BY-NC-SA 3.0)

§📄 License

MIT — see LICENSE. Dictionary data: Kateglo (CC-BY-NC-SA 3.0) — non-commercial use only for the bundled Indonesian word list. Javanese dictionary: Riza et al. 2018 (CC-BY 4.0).

Modules§

javanese

Structs§

Dictionary: A dictionary used for stemming and stopword lookups.
MorphAnalysis: Detected morphological components of a word.
MorphAnalyzer: Dictionary-free morphological analyzer for Bahasa Indonesia.
Stemmer: The main Indonesian word stemmer.
StopWord: Legacy stopword filter used for stripping common function words from sentences.
Tokenizer: A zero-copy tokenizer for Indonesian text.

Crate sastrawi

Crate sastrawi Copy item path

§sastrawi-rs

§Quick Start

§🇮🇩 Indonesian Stemmer

§What’s New (vs. original rust-sastrawi)

§Usage

§Stopword Filtering

§Custom Dictionary

§Indonesian API Reference

§Indonesian Stemming Pipeline (Nazief-Adriani + ECS)

§🫙 Javanese Stemmer (Bahasa Jawa) 🆕

§Usage

§Custom Dictionary

§Javanese Affix Rules Reference

§1. Ater-ater Anuswara (Nasalization) [⁶]

§2. Ater-ater Tripurusa & General Prefixes

§3. Panambang (Suffixes) & Allomorphs

§4. Circumfix Backtracking (Confiks)

§Javanese API Reference

§Javanese Stemming Pipeline

§🔬 MorphAnalyzer — Dictionary-Free Morphological Analyzer

§When to use MorphAnalyzer vs Stemmer

§Usage

§MorphAnalysis struct

§Detection pipeline (priority order)

§Known limitations (by design)

§MorphAnalyzer API

§🏗 Architecture

§📊 Performance

§🧪 Testing

§Indonesian Suites

§Javanese Suites

§🆕 Indonesian Extensions (2020–2026 Research)

§A. nge- Informal Prefix

§B. Confixes — ECS (Enhanced Confix Stripping)

§C. Superlative se-nya Particle

§D. Loanword Suffixes

§📚 References & Credits

§Indonesian Stemmer

§Javanese Stemmer

§General

§📄 License

Modules§

Structs§

Crate sastrawi

§When to use `MorphAnalyzer` vs `Stemmer`

§`MorphAnalysis` struct

§A. `nge-` Informal Prefix

§C. Superlative `se-nya` Particle