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Contraband Police Subtitle
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LOCALIZATION MOD
RELEASED vRC-1 Austronesian Lang

Contraband Police Subtitle Contraband Police Subtitle

Bahasa Indonesia, Melayu, Filipino

Memuat data interpretasi naratif secara real-time...

Product Narrative

The Full Story

Selamat datang di Republik Rakyat Acaristan tahun 1980, tempat lo bakal berperan jadi petugas bea cukai di pos Karikatka yang dingin dan brutal. Tugas lo simpel tapi mematikan: periksa dokumen, bongkar kendaraan buat nyari kontraband kayak opium atau uang palsu, dan pastiin gerombolan Blood Fist nggak bikin kacau. Tiap pilihan lo punya konsekuensi berat, mau jadi petugas jujur yang setia sama partai atau nerima suap biar dompet makin tebal?


Nah biar makin kerasa hawa Orde Baru-nya Acaristan, gue udah buatin mod lokalisasi yang bukan kaleng-kaleng! Pake teknologi neural pipeline 8-tahap, gue proses 21.430 kata biar bahasanya nendang banget dan nggak kaku kayak hasil translasi otomatis. Bahasa di sini disesuaikan ama gaya ngomong tiap karakter, dari komandan yang galak sampe penyelundup yang licin, semua pake slang lokal kita yang asik. Kalo mau ngerasain sensasi razia dan interogasi paling imersif dalem Bahasa Indonesia, Melayu, atau Filipina, mod ini wajib banget lo gas!

Current Milestone

Available Now

Author's Notes

=== Audit Teknis & Semantik Lokalisasi CONTRABAND POLICE ===

1. SKALA LINGUISTIK & CAKUPAN

- Skala Proyek: Sekitar 21,430 kata diproses melalui alur neural 8-tahap.

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

- Status Kelengkapan: Indonesia: 97.2%, Malay: 98.0%, Filipino: 88.7%

- Analisis Variasi Leksikal: Source -> Density: 64.7% | Diversity: 15.2%, Indonesia -> Density: 75.5% | Diversity: 19.4%, Malay -> Density: 73.4% | Diversity: 16.6%, Filipino -> Density: 60.4% | Diversity: 17.1%


2. VALIDASI NEURAL & AKURASI

- Skor Keselarasan Semantik (Platt Score): Indonesia: 88%, Malay: 87%, Filipino: 86%

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

- Gaya Bahasa Karakter: Penyesuaian gaya (gaul, formal, santai) telah diterapkan pada 1 karakter unik.

- Pemulihan Struktur Otomatis (Tag Repair): 11 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.

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
28.2%
Standard
70.0%
Formal
1.8%
Emotional Spectrum

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

Neutral/Functional
48.8%
Stoic/Restrained
26.2%
Positive/Warm
13.3%
Negative/Intense
8.0%
Complex/Ambivalent
3.7%
Archetypes
9 detected
Ui
35.6%
Global
20.8%
Sergeant Maximov
9.8%
Inspector Sorokin
9.7%
Npc
7.8%
The Inspector
6.7%
Yuri Petrov
6.0%
Michail Garin
2.2%
System
1.4%

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
1,611 / 1,657 lines
97%
Semantic Sim.
88 %
Lex. Density
75.5 %
src
64.7%
Lex. Diversity
19.4 %
src
15.2%
MS
Malay
1,624 / 1,657 lines
98%
Semantic Sim.
87 %
Lex. Density
73.4 %
src
64.7%
Lex. Diversity
16.6 %
src
15.2%
TL
Tagalog
1,469 / 1,657 lines
89%
Semantic Sim.
86 %
Lex. Density
60.4 %
src
64.7%
Lex. Diversity
17.1 %
src
15.2%

* 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
4,539 Token Lines
Src Density
64.7%
Src Diversity
15.2%
Syntactic Error Report

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

11
Mismatch
11
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) 2026-04-22 21:07
Tag Repair (S6) 2026-04-22 13:04
Validator (S5) 2026-04-22 13:03
Corrector (S3) 2026-04-22 12:59
Re-Import (S4) 2026-04-22 12:59
Translator (S2) 2026-04-22 12:17
Tagger (S1) 2026-04-22 12:08
Splitter (S0) 2026-04-22 11:56

Released Archive

Austronesian Showcase

Location
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