Game-Translator
Batman Arkham Knight Mod
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LOCALIZATION MOD
WATERMARKED valpha-1 Austronesian Lang

Batman Arkham Knight Mod Batman Arkham Knight Mod

Bahasa Indonesia, Melayu, Filipino

Unduh mod translasi Batman Arkham Knight Bahasa Indonesia! Mainkan game aksi stealth legendaris ini dengan dialog slang lokal dan gaya bahasa super akurat.

Product Narrative

The Full Story

Hujan di Gotham City tidak pernah berhenti, begitu juga dengan ancaman para penjahat. Di Batman: Arkham Knight, kamu bakal jadi sang Kesatria Kegelapan yang berhadapan langsung dengan Scarecrow dan pasukan Arkham Knight. Kebayang nggak sih rasanya ngehajar preman-preman kota yang lagi ngoceh kasar pakai bahasa gaul dan umpatan lokal pas kamu kejar pakai Batmobile? Proyek lokalisasi ini mengolah 250.652 kata lewat 8-stage neural pipeline canggih yang bikin gaya bahasanya dinamis banget! Alfred tetap sopan dan loyalis, sementara kroco jalanan bakal maki-maki sekasar mungkin layaknya preman asli. Harap diingat kalau mod ini berstatus EXPERIMENTAL ALPHA dan masih ada watermark tertanam untuk keperluan tes, tapi game ini sudah diterjemahkan 94,7% buat bahasa Indonesia dan 98,7% buat Melayu! Yuk, unduh sekarang dan kasih tahu kami pendapatmu!

Current Milestone

Experimental Build

Author's Notes

=== Audit Teknis & Semantik Lokalisasi BATMAN ARKHAM KNIGHT === 1. SKALA LINGUISTIK & CAKUPAN - Skala Proyek: Sekitar 250,652 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: 96.0%, Malay: 96.1%, Filipino: 93.7% - Analisis Variasi Leksikal: Source -> Density: 66.6% | Diversity: 4.8%, Indonesia -> Density: 71.3% | Diversity: 6.4%, Malay -> Density: 75.3% | Diversity: 5.0%, Filipino -> Density: 61.3% | Diversity: 6.2% 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 128 karakter unik. - Pemulihan Struktur Otomatis (Tag Repair): 0 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.

Attention: This version contains 7.0% watermarks. Support this project on Trakteer or Ko-fi to download NON-WATERMARKED version.

Comments

Max 2000 chars · 10/hour · Change name via the chat icon

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
62.6%
Standard
10.0%
Formal
27.4%
Emotional Spectrum

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

Stoic/Restrained
34.5%
Neutral/Functional
21.4%
Negative/Intense
18.7%
Complex/Ambivalent
14.0%
Positive/Warm
11.4%
Archetypes
30 detected
Ambient Character
37.8%
Ui/system
30.5%
Npc Militia
11.2%
Npc Combatthug
6.9%
Npc Thug
2.5%
Riddler
2.3%
Npc Shots Militia
0.8%
Npc Mp Cop
0.7%
Npc Gn Cop
0.5%
Scarecrow
0.5%
Npc Cars Thugs
0.4%
Npc Shots Thug
0.4%
Npc Twofacethug
0.3%
Batman
0.3%
Npc Arm Militia
0.2%
Npc Arm Thug
0.2%
Npc Peng Thug
0.2%
Npc Diner Cop
0.2%
Npc Wheel Militia
0.2%
Penguin
0.2%
Two Face
0.2%
Npc Diner Thug
0.1%
Npc Post Cop
0.1%
Npc Cityx Militia
0.1%
Npc Thugs Cop
0.1%
Joker
0.1%
Npc Harley Thug
0.1%
Npc Gordon Militia
0.1%
Npc Tf Thug
0.1%
Npc Forensics Militia
0.1%

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
24,802 / 25,848 lines
96%
Semantic Sim.
88 %
Lex. Density
71.3 %
src
66.6%
Lex. Diversity
6.4 %
src
4.8%
MS
Malay
24,831 / 25,848 lines
96%
Semantic Sim.
86 %
Lex. Density
75.3 %
src
66.6%
Lex. Diversity
5.0 %
src
4.8%
TL
Tagalog
24,218 / 25,848 lines
94%
Semantic Sim.
84 %
Lex. Density
61.3 %
src
66.6%
Lex. Diversity
6.2 %
src
4.8%

* 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
74,094 Token Lines
Src Density
66.6%
Src Diversity
4.8%
Syntactic Error Report

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

49
Mismatch
49
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

Splitter (S0) 2026-10-06 00:32
Merger (S7) 2026-10-05 18:43
Translator (S2) 2026-10-04 21:24
Tag Repair (S6) 2026-10-04 20:12
Re-Import (S4) 2026-10-04 20:04
Corrector (S3) 2026-10-04 20:02
Tagger (S1) 2026-10-04 14:57
Validator (S5) 2026-09-30 10:00

Released Archive

Austronesian Showcase

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