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Rune Factory Guardian Of Azuma Mod
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LOCALIZATION MOD
WATERMARKED valpha-1 Austronesian Lang

Rune Factory Guardian Of Azuma Mod Rune Factory Guardian Of Azuma Mod

Bahasa Indonesia, Melayu, Filipino

Mainkan Rune Factory: Guardians of Azuma Bahasa Indonesia dengan mod terjemahan 515 ribu kata yang bikin obrolan warga desa makin seru dan natural!

Product Narrative

The Full Story

Rune Factory: Guardians of Azuma mengajak kamu bertualang sebagai Earth Dancer di dunia yang hancur karena wabah Blight. Berbekal tabuhan genderang mistis, kamu bertugas mengembalikan energi murni desa, membangun kembali kuil suci, dan tentu saja bercocok tanam sambil menjalin asmara dengan karakter-karakter unik seperti Kaguya sahabat masa kecilmu. Main game JRPG dengan bahasa aslinya memang asyik, tapi mod lokalisasi ini siap bikin pengalaman main kamu jauh lebih nendang! Saya menyiksa mesin neural 8-tahap buatan sendiri untuk menerjemahkan 515.442 kata, mencetak rekor persentase 96,8% untuk bahasa Indonesia dengan gaya bahasa santai dan luwes ala tongkrongan lokal. Tiap karakter punya gaya bicaranya sendiri, jadi obrolan bareng warga desa bakal terasa super natural. Ingat, ini masih versi Experimental Alpha dengan pratinjau ber-watermark, jadi unduh sekarang, nikmati terjemahan ajaib ini, dan tolong kasih tahu saya kalau ada kalimat yang masih terdengar aneh!

Current Milestone

Experimental Build

Author's Notes

=== Audit Teknis & Semantik Lokalisasi RUNE FACTORY GUARDIAN OF AZUMA === 1. SKALA LINGUISTIK & CAKUPAN - Skala Proyek: Sekitar 515,442 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: 96.8%, Malay: 96.9%, Filipino: 95.3% - Analisis Variasi Leksikal: Source -> Density: 64.9% | Diversity: 2.5%, Indonesia -> Density: 73.6% | Diversity: 3.4%, Malay -> Density: 75.7% | Diversity: 2.7%, Filipino -> Density: 61.1% | Diversity: 3.5% 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 42 karakter unik. - Pemulihan Struktur Otomatis (Tag Repair): 4279 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

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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
62.1%
Standard
23.5%
Formal
14.4%
Emotional Spectrum

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

Positive/Warm
42.6%
Stoic/Restrained
23.4%
Neutral/Functional
14.5%
Complex/Ambivalent
13.0%
Negative/Intense
6.5%
Archetypes
30 detected
Ambient Character
58.1%
Ui/system
19.8%
Subaru
1.2%
Kaguya
1.1%
Murasame
1.0%
Fubuki
1.0%
Ulalaka
1.0%
Ikaruga
1.0%
Kai
1.0%
Matsuri
1.0%
Kanata
1.0%
Mauro
1.0%
Hina
0.9%
Iroha
0.9%
Kurama
0.9%
Cuilang
0.9%
Clarice
0.9%
Pilika
0.9%
Hisui
0.5%
Tsubame
0.4%
Yachiyo
0.4%
Watarase
0.4%
Zaza
0.4%
Takumi
0.4%
Kusatsu
0.4%
Sakaki
0.4%
Suzu
0.4%
Kotaro
0.4%
Tsuyu
0.3%
Riku
0.3%

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
52,765 / 54,489 lines
97%
Lex. Density
73.6 %
src
64.9%
Lex. Diversity
3.4 %
src
2.5%
MS
Malay
52,825 / 54,489 lines
97%
Lex. Density
75.7 %
src
64.9%
Lex. Diversity
2.7 %
src
2.5%
TL
Tagalog
51,904 / 54,489 lines
95%
Lex. Density
61.1 %
src
64.9%
Lex. Diversity
3.5 %
src
2.5%

* 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
163,299 Token Lines
Src Density
64.9%
Src Diversity
2.5%
Syntactic Error Report

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

4279
Mismatch
4279
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) 2026-10-03 15:30
Tag Repair (S6) 2026-10-03 11:12
Re-Import (S4) 2026-10-03 09:27
Corrector (S3) 2026-10-03 09:25
Translator (S2) 2026-10-03 09:03
Tagger (S1) 2026-09-30 03:18
Splitter (S0) 2026-09-29 17:32

Released Archive

Austronesian Showcase

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