Game-Translator
Persona 5 Royal Mod
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LOCALIZATION MOD
WATERMARKED valpha-3 Austronesian Lang

Persona 5 Royal Mod Persona 5 Royal Mod

Bahasa Indonesia, Melayu, Filipino

Unduh mod terjemahan bahasa Indonesia Persona 5 Royal sekarang dan nikmati petualangan Phantom Thieves dengan dialog gaul penuh slang lokal!

Product Narrative

The Full Story

Jadilah Joker dan pimpin Phantom Thieves menjelajahi gemerlap kota Tokyo demi mengubah hati para penjahat berkuasa! Di Persona 5 Royal, kamu harus membagi waktu antara kehidupan sekolah yang seru dan petualangan menegangkan di Metaverse untuk mencuri hasrat terdistorsi dari targetmu. Bosan dengan teks bahasa Inggris yang kaku? Mod lokalisasi ini mengolah lebih dari 1,4 juta kata lewat 8-stage neural pipeline demi menghadirkan dialog Ryuji yang ngegas, ocehan Morgana yang setia, serta gosip anak Shujin pake ungkapan dan slang lokal yang pas banget! Saat ini mod hadir dalam fase Experimental Alpha lengkap dengan watermark preview—yuk unduh sekarang dan bantu kami bikin lokalisasi ini makin mantap!

Current Milestone

Experimental Build

Author's Notes

=== Audit Teknis & Semantik Lokalisasi PERSONA 5 ROYAL === 1. SKALA LINGUISTIK & CAKUPAN - Skala Proyek: Sekitar 1,497,518 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%, Filipino: 96.2% - Analisis Variasi Leksikal: Source -> Density: 70.8% | Diversity: 3.5%, Indonesia -> Density: 77.2% | Diversity: 3.4%, Filipino -> Density: 67.8% | Diversity: 3.3% 2. VALIDASI NEURAL & AKURASI - Skor Keselarasan Semantik (Platt Score): Indonesia: 69%, Malay: 70%, Filipino: 71% (Skor ini mengukur seberapa akurat terjemahan mempertahankan makna asli dari teks sumber.) - Gaya Bahasa Karakter: Penyesuaian gaya (gaul, formal, santai) telah diterapkan pada 2724 karakter unik. - Pemulihan Struktur Otomatis (Tag Repair): 4655 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 3.4% 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
41.7%
Standard
43.2%
Formal
15.1%
Emotional Spectrum

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

Neutral/Functional
28.7%
Complex/Ambivalent
23.8%
Positive/Warm
22.9%
Stoic/Restrained
17.8%
Negative/Intense
6.8%
Archetypes
30 detected
Ambient Character
23.6%
Morgana
9.4%
Ryuji
6.1%
Ui/system
5.3%
Makoto
4.4%
Ann
4.4%
Yusuke
3.4%
Futaba
3.1%
Haru
2.3%
Akechi
2.2%
Sojiro
1.7%
Kawakami
1.2%
Maruki
1.1%
Mishima
1.1%
Sumire
1.0%
Chihaya
1.0%
Ohya
0.9%
Kasumi
0.9%
Takemi
0.8%
Hifumi
0.8%
Sae
0.8%
Caroline
0.7%
Newscaster
0.7%
Justine
0.6%
Iwai
0.6%
坂本 竜司
0.5%
Shinya
0.5%
Yoshida
0.4%
Man With Clear Eyes
0.4%
Lavenza
0.4%

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
168,052 / 173,744 lines
97%
Semantic Sim.
69 %
Lex. Density
77.2 %
src
70.8%
Lex. Diversity
3.4 %
src
3.5%
TL
Tagalog
167,216 / 173,744 lines
96%
Semantic Sim.
71 %
Lex. Density
67.8 %
src
70.8%
Lex. Diversity
3.3 %
src
3.5%
MS
Malay
Semantic Sim.
70 %

* 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
521,168 Token Lines
Src Density
70.8%
Src Diversity
3.5%
Syntactic Error Report

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

4655
Mismatch
4655
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) 2026-10-16 13:11
Tag Repair (S6) 2026-10-16 11:10
Re-Import (S4) 2026-10-16 03:33
Corrector (S3) 2026-10-16 02:22
Tagger (S1) 2026-10-15 10:41
Translator (S2) 2026-10-11 01:02
Validator (S5) 2026-10-10 07:39
Splitter (S0) 2026-10-07 17:30
Tagger (S1) 2026-03-11 17:25
Tagger (S1) 2026-03-09 05:35

Released Archive

Austronesian Showcase

Location
Image
Video