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GTA V Enhanced & Legacy Subtitle
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LOCALIZATION MOD
WATERMARKED vExperimental-2 Austronesian Lang

GTA V Enhanced & Legacy Subtitle GTA V Enhanced & Legacy Subtitle

Bahasa Indonesia, Melayu, Filipino

Experience entertainment blockbusters Grand Theft Auto V and Grand Theft Auto Online — now upgraded for a new generation with stunning visuals, faster loading, 3D audio, and more, plus exclusive content for GTA Online players.

Product Narrative

The Full Story

Rasakan kekacauan di Los Santos bareng Michael si pensiunan perampok, Franklin sang penggerutu jalanan, dan Trevor si psikopat paling ikonik dalam mahakarya open-world dari Rockstar. Dari ngerampok bank sampe dikejar militer, GTA V adalah puncak dari aksi gila dan komedi satir yang gak bakal lo temuin di game lain.


Gue tau lo capek liat terjemahan kaku yang kayak tugas sekolah, makanya gue garap mod ini buat lo! Dengan lebih dari satu juta kata (1,074,122 tepatnya) yang diproses lewat engine AI 8-tahap, lo bakal dapet dialog yang 'bernyawa'. Bacotannya Lamar, emosinya Michael, sampe gilanya Trevor semua dapet feel 'anak tongkrongan' asli SEA. Gak perlu bingung lokasi atau nama senjata karena UI tetep English biar gampang ikutin guide, tapi ceritanya? Udah pasti full-power Bahasa Indonesia, Melayu, ama Filipino yang gaul banget. Sikat sebelum diculik Trevor!

Current Milestone

Experimental Build

Author's Notes

Supported GTA V Legacy and Enhanced


=== Audit Teknis & Semantik Lokalisasi GTA V ENHANCED ===

1. SKALA LINGUISTIK & CAKUPAN

- Skala Proyek: Sekitar 1,074,122 kata diproses melalui alur neural 8-tahap.

- Cakupan Bahasa: Dukungan trilingual penuh untuk pasar Indonesia, Malaysia, dan Filipina.

- Status Kelengkapan: Indonesia: 98.7%, Malay: 98.9%, Filipino: 96.9%

- Analisis Variasi Leksikal: Source -> Density: 65.6% | Diversity: 2.2%, Indonesia -> Density: 76.8% | Diversity: 2.9%, Malay -> Density: 76.1% | Diversity: 2.2%, Filipino -> Density: 62.5% | Diversity: 2.9%


2. VALIDASI NEURAL & AKURASI

- Skor Keselarasan Semantik (Platt Score): Indonesia: 87%, Malay: 85%, Filipino: 84%

(Skor ini mengukur seberapa akurat terjemahan mempertahankan makna asli dari teks sumber.)

- Gaya Bahasa Karakter: Penyesuaian gaya (gaul, formal, santai) telah diterapkan pada 711 karakter unik.

- Pemulihan Struktur Otomatis (Tag Repair): 565 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 2.7% watermarks. Support this project on Trakteer or Ko-fi to download NON-WATERMARKED version.

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
64.0%
Standard
29.7%
Formal
6.4%
Emotional Spectrum

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

Neutral/Functional
40.6%
Stoic/Restrained
21.9%
Positive/Warm
14.7%
Negative/Intense
13.1%
Complex/Ambivalent
9.7%
Archetypes
30 detected
System/ui
45.7%
Michael
8.3%
Trevor
7.9%
Franklin
6.2%
Lamar
2.8%
Jimmy
2.4%
Npc
1.8%
Lester
1.5%
Packie
1.4%
Dom
0.9%
Amanda
0.7%
Nervousron
0.6%
Tracey
0.6%
Pilotdispatch
0.6%
Ui
0.5%
Beverly
0.5%
Dave
0.5%
Oscar
0.4%
Cletus
0.4%
Floyd
0.4%
Steve
0.4%
Norm
0.4%
Gustavo
0.4%
Tonya
0.3%
Wade
0.3%
Daryl
0.3%
Joe
0.2%
Misterk
0.2%
Liengineer
0.2%
Maryann
0.2%

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
108,024 / 109,469 lines
99%
Semantic Sim.
87 %
Lex. Density
76.8 %
src
65.6%
Lex. Diversity
2.9 %
src
2.2%
MS
Malay
108,251 / 109,469 lines
99%
Semantic Sim.
85 %
Lex. Density
76.1 %
src
65.6%
Lex. Diversity
2.2 %
src
2.2%
TL
Tagalog
106,106 / 109,469 lines
97%
Semantic Sim.
84 %
Lex. Density
62.5 %
src
65.6%
Lex. Diversity
2.9 %
src
2.2%

* 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
325,650 Token Lines
Src Density
65.6%
Src Diversity
2.2%
Syntactic Error Report

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

565
Mismatch
564
Fixed
1
Partial

Name

Label
Retrieving Portrait...
Narrative Profile

Associated Entities
Semantic Archetypes

NLP Pipeline Intelligence

Video Logs

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) 2026-04-12 08:57
Tag Repair (S6) 2026-04-01 03:50
Validator (S5) 2026-04-01 02:57
Re-Import (S4) 2026-04-01 01:33
Corrector (S3) 2026-04-01 01:30
Translator (S2) 2026-04-01 00:50
Tagger (S1) 2026-03-31 17:10
Splitter (S0) 2026-03-31 12:33

Released Archive

Austronesian Showcase

Location
Image
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