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LOCALIZATION MOD
WATERMARKED vExperimental-1 Austronesian Lang

Black Flag Subtitle Black Flag Subtitle

Bahasa Indonesia, Melayu, Filipino

Memuat data interpretasi naratif secara real-time...

Product Narrative

The Full Story

Halo para pelaut digital! Kalau kalian bosan sama terjemahan game yang kaku kayak bapak-bapak lagi baca teks pidato, tenang saja karena kalian sudah berlabuh di tempat yang tepat. Kami tidak main-main dalam mengerjakan proyek ini; kami membangun sistem neural pipeline 8-tahap rahasia untuk menerjemahkan lebih dari 203 ribu kata di Assassin's Creed Black Flag Resynced ke Bahasa Indonesia, Melayu, dan Filipino dengan kualitas yang luar biasa alami.


Obrolan kasar para bajak laut seperti Edward Kenway dan Charles Vane bakal terasa sangat luwes dengan umpatan khas lokal yang pas, sementara percakapan ordo Templar serta Animus modern tetap disajikan dengan rapi. Proyek ini murni lahir dari kecintaan kami terhadap komunitas gamer nusantara dengan tingkat penyelesaian mencapai 98 persen tanpa error tag. Langsung saja download mod gratis ini sekarang dan rasakan serunya mengarungi lautan Karibia dengan bahasa lokal yang pecah banget!

Current Milestone

Experimental Build

Author's Notes

=== Audit Teknis & Semantik Lokalisasi ASSASSIN'S CREED BLACK FLAG RESYNCED ===


1. SKALA LINGUISTIK & CAKUPAN

- Skala Proyek: Sekitar 203,349 kata diproses melalui alur neural 8-tahap.

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

- Status Kelengkapan: Indonesia: 98.1%, Malay: 98.2%, Filipino: 98.0%

- Analisis Variasi Leksikal: Source -> Density: 65.4% | Diversity: 7.1%, Indonesia -> Density: 72.5% | Diversity: 8.4%, Malay -> Density: 73.5% | Diversity: 7.5%, Filipino -> Density: 60.9% | Diversity: 7.8%


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 2036 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 2.8% 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
62.7%
Standard
17.8%
Formal
19.5%
Emotional Spectrum

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

Stoic/Restrained
26.2%
Positive/Warm
21.0%
Neutral/Functional
20.5%
Negative/Intense
16.4%
Complex/Ambivalent
16.0%
Archetypes
30 detected
Smuggler
9.4%
Edward Kenway & Charles Vane
8.5%
Edward Kenway & Anto
7.6%
Edward Kenway
6.4%
Anto
6.1%
Npc
6.1%
Town Drunk
6.1%
Ah Tabai
4.3%
Edward Kenway & Blackbeard
4.1%
Happy Ending
4.0%
Edward Kenway & Travers Brothers
3.6%
Contractaccepted
2.5%
Fortcaptured
2.1%
Stede Bonnet
1.8%
That Bastard Maynard
1.8%
Opia Apito
1.7%
Adewale
1.7%
Bartholomew Roberts
1.5%
Ui
1.4%
Shop Treasure Dealer
1.3%
Edward Thatch (blackbeard)
1.3%
Charles Vane
1.3%
Plantainandplantation
1.0%
Mary Read (james Kidd)
1.0%
Benjamin Hornigold
0.9%
Assassination Target
0.9%
Kill Chamberlaine
0.9%
The Trial
0.8%
Laureano De Torres
0.8%
Julien Du Casse
0.7%

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
12,327 / 12,561 lines
98%
Lex. Density
72.5 %
src
65.4%
Lex. Diversity
8.4 %
src
7.1%
MS
Malay
12,339 / 12,561 lines
98%
Lex. Density
73.5 %
src
65.4%
Lex. Diversity
7.5 %
src
7.1%
TL
Tagalog
12,309 / 12,561 lines
98%
Lex. Density
60.9 %
src
65.4%
Lex. Diversity
7.8 %
src
7.1%

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

Name

Label
Retrieving Portrait...
Narrative Profile

Associated Entities
Semantic Archetypes

NLP Pipeline Intelligence

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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) 2026-09-13 06:04
Re-Import (S4) 2026-09-13 01:51
Corrector (S3) 2026-09-13 01:24
Translator (S2) 2026-09-12 19:19
Tagger (S1) 2026-09-12 12:49
Splitter (S0) 2026-09-12 09:38

Released Archive

Austronesian Showcase

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