Game-Translator
The Witcher 3 Mod
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LOCALIZATION MOD
WATERMARKED valpha-3 Austronesian Lang

The Witcher 3 Mod The Witcher 3 Mod

Bahasa Indonesia, Melayu, Filipino

Mainkan The Witcher 3 Bahasa Indonesia dengan mod translasi gaul yang bikin dialog Geralt of Rivia makin asik dan natural tanpa bikin pusing.

Product Narrative

The Full Story

The Witcher 3 itu mahakarya RPG di mana kamu main sebagai Geralt of Rivia, seorang pemburu monster sinis yang cuma pengen nyari anak angkatnya, Ciri, sebelum dunia kiamat kena White Frost. Siap-siap aja ngabisin ratusan jam buat nebas Griffin, main kartu Gwent di tengah perang, dan pusing mikirin pilihan moral yang nggak ada benernya. Gamenya gelap, brutal, dan punya cerita yang dijamin bikin merinding. Tapi jujur aja, dengerin sarkasmenya Geralt bakal jauh lebih ngena kalau bahasanya asik dan natural! Aku ngebangun pipeline AI 8-tahap sampai begadang mampus buat nerjemahin lebih dari 1 juta kata, biar umpatan NPC dan obrolan warung di Velen kerasa pas sama kultur kita. Mod ini punya gaya bahasa dinamis yang bedain ngobrol santai sama bahasa formal kerajaan, dengan tingkat terjemahan udah tembus 96%. Ini bukan sekadar translate kaku dari Google, tapi murni proyek ambisius biar kalian bisa main The Witcher 3 dengan bahasa gaul yang nggak kaku. Gas install, dan selamat berburu monster!

Current Milestone

Experimental Build

Author's Notes

=== Audit Teknis & Semantik Lokalisasi THE WITCHER 3 === 1. SKALA LINGUISTIK & CAKUPAN - Skala Proyek: Sekitar 1,070,327 kata diproses melalui alur neural 8-tahap. - Cakupan Bahasa: Dukungan trilingual penuh untuk pasar Indonesia, Malaysia, dan Filipina. - Status Build: Experimental Alpha — watermarked preview build. - Status Kelengkapan: Indonesia: 96.7% - Analisis Variasi Leksikal: Source -> Density: 64.5% | Diversity: 2.9%, Indonesia -> Density: 73.1% | Diversity: 3.4% 2. VALIDASI NEURAL & AKURASI - Skor Keselarasan Semantik (Platt Score): (Skor ini mengukur seberapa akurat terjemahan mempertahankan makna asli dari teks sumber.) - Gaya Bahasa Karakter: Penyesuaian gaya (gaul, formal, santai) telah diterapkan pada 338 karakter unik. - Pemulihan Struktur Otomatis (Tag Repair): 256 tag kode game telah dipulihkan secara presisi. 3. KAPABILITAS ENGINE - Pipeline: Austronesian Localization System (Neural LoRA-Adaptive Architecture). - Pengenalan Entitas: Ekstraksi penuh untuk terminologi spesifik game dan konstanta lore.

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Comments

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Linguistic Analysis Report

Stylometric Register Analysis

Discourse analysis using Gemma embeddings. Classifies rhetorical register across the corpus to ensure tonal consistency with source narrative assets.

Casual
57.9%
Standard
27.8%
Formal
14.2%
Emotional Spectrum

Emotional tone mapped via dot-product similarity between extracted dialog embeddings and predefined sentiment anchors using zero-shot semantic alignment.

Stoic/Restrained
32.0%
Neutral/Functional
18.7%
Positive/Warm
17.4%
Negative/Intense
16.1%
Complex/Ambivalent
15.8%
Archetypes
30 detected
Geralt
26.9%
Ui/system
24.9%
Peasant
2.0%
Yennefer
1.4%
Thug
1.2%
Cirilla
1.2%
Bandit
1.2%
Citizen
1.1%
Old Man
1.1%
Triss
0.9%
Poor
0.9%
Noble
0.9%
Woman
0.8%
Rich
0.8%
Warrior
0.8%
Nilfgaardian
0.8%
Temerian
0.8%
Villager
0.7%
Regis
0.7%
Old Woman
0.7%
Guard
0.7%
Knight
0.6%
Bastard
0.6%
Shani
0.5%
Boy
0.5%
Keira Metz
0.5%
Lambert
0.5%
Baron
0.5%
Dandelion
0.5%
Anna Henrietta
0.5%

DISCLOSURE: Profiling data generated algorithmically via zero-shot inference and semantic vector alignment. Represents AI interpretation of the dataset corpus, not explicit ground-truth statistics from the underlying game engine or internal metrics. Use as a heuristic guide for context mapping.

Cross-Lingual Quality Matrix

Semantic alignment quantified via Multilingual E5 Large Instruct (RoBERTa based) bitext mining. NER entities preserved using GLiNER heuristic extraction protocols to maintain terminological invariance.

ID
Indonesian
83,182 / 85,901 lines
97%
Semantic Sim.
67 %
Lex. Density
73.1 %
src
64.5%
Lex. Diversity
3.4 %
src
2.9%
MS
Malay
Semantic Sim.
66 %
TL
Tagalog
Semantic Sim.
68 %

* Sim = Cosine Similarity (Vector Space) · Density = Content/Total Tokens · Diversity = TTR (Type-Token Ratio) · "src" = Source Baseline · Named Entities enforced via GLiNER mining.

Corpus Volume & Metrics
256,212 Token Lines
Src Density
64.5%
Src Diversity
2.9%
Syntactic Error Report

Heuristic markup verification utilizing multi-pass validation and correction to ensure syntactical integrity of control codes and visual tags.

256
Mismatch
256
Fixed
0
Partial

Name

Label
Retrieving Portrait...
Narrative Profile

Associated Entities
Semantic Archetypes

NLP Pipeline Intelligence

Featured Preview Auto-Detected

Line Identity 0
Source (English)
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Indonesian (ID)
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Malay (MS)
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Tagalog (TL)
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Pipeline Receipts

Merger (S7) 2026-10-10 23:01
Validator (S5) 2026-10-09 18:44
Re-Import (S4) 2026-10-06 17:34
Corrector (S3) 2026-10-06 17:18
Tag Repair (S6) 2026-10-05 04:44
Translator (S2) 2026-10-05 02:45
Tagger (S1) 2026-10-04 14:39
Splitter (S0) 2026-10-04 12:58

Released Archive

Austronesian Showcase

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