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Assassin's Creed Valhalla Mod
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LOCALIZATION MOD
WATERMARKED valpha-3 Austronesian Lang

Assassin's Creed Valhalla Mod Assassin's Creed Valhalla Mod

Bahasa Indonesia, Melayu, Filipino

Main Assassin's Creed Valhalla Bahasa Indonesia dengan mod lokalisasi 745.180 kata! Nikmati dialog slang brutal Eivor yang bikin petualangan makin seru.

Product Narrative

The Full Story

Pernah bayangkan Eivor ngomong pake slang lokal yang bikin musuh gemetar? Lupakan terjemahan kaku yang kayak buku sejarah sekolah! Mod ini memproses 745.180 kata lewat engine neural 8-tahap yang gila banget buat mastiin klan Raven ngomongnya luwes, kasar, dan penuh wibawa. Eivor, Sigurd, sampai Basim sekarang punya 'nyawa' lewat dialog yang disesuaikan dengan kultur kita, lengkap dengan umpatan yang pas buat suasana perang di Inggris kuno. Tapi ingat, ini proyek jujur dari modder yang kurang tidur, jadi statusnya masih Experimental Alpha. Bakal ada watermark pratinjau yang sesekali lewat, dan mungkin ada beberapa kalimat yang masih terasa aneh di telinga. Tapi hey, progresnya sudah 98% lebih untuk Bahasa Indonesia, Melayu, dan Filipino! Ayo bantu kami sempurnakan mod ini dengan cara kasih feedback kalau nemu kalimat yang 'ngaco'. Sikat habis musuhmu dan nikmati Valhalla versi kearifan lokal!

Current Milestone

Experimental Build

Author's Notes

=== Audit Teknis & Semantik Lokalisasi ASSASSIN'S CREED VALHALLA === 1. SKALA LINGUISTIK & CAKUPAN - Skala Proyek: Sekitar 745,180 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: 98.7%, Malay: 98.8%, Filipino: 98.4% - Analisis Variasi Leksikal: Source -> Density: 62.2% | Diversity: 3.1%, Indonesia -> Density: 70.4% | Diversity: 4.2%, Malay -> Density: 70.8% | Diversity: 3.6%, Filipino -> Density: 58.6% | Diversity: 3.9% 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 37 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 2.6% 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
49.3%
Standard
11.6%
Formal
39.0%
Emotional Spectrum

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

Positive/Warm
27.1%
Stoic/Restrained
25.7%
Neutral/Functional
19.4%
Negative/Intense
17.1%
Complex/Ambivalent
10.7%
Archetypes
30 detected
Ambient Character
71.2%
Eivor (male)
8.5%
Eivor Varinsdottir
4.5%
King Ceolwulf Ii
3.0%
Sigurd Styrbjornsson
2.8%
Basim
1.5%
Alfred The Great
1.4%
Lost Drengr
1.1%
Halfdan Ragnarsson
1.1%
Randvi
0.6%
Soma
0.5%
Hemming Jarl
0.5%
Ubba Ragnarsson
0.4%
Fulke
0.4%
Guthrum
0.4%
Varin
0.4%
Ivarr Ragnarsson
0.3%
Valka
0.2%
Rollo
0.2%
Dag
0.2%
Gunnar
0.1%
Merchant
0.1%
Hytham
0.1%
Orlog Player
0.1%
Soldier / Guard
0.1%
Hidden One
0.0%
Odin (havi)
0.0%
Blacksmith
0.0%
Flyting Rival
0.0%
Child
0.0%

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
51,481 / 52,155 lines
99%
Semantic Sim.
89 %
Lex. Density
70.4 %
src
62.2%
Lex. Diversity
4.2 %
src
3.1%
MS
Malay
51,514 / 52,155 lines
99%
Semantic Sim.
88 %
Lex. Density
70.8 %
src
62.2%
Lex. Diversity
3.6 %
src
3.1%
TL
Tagalog
51,345 / 52,155 lines
98%
Semantic Sim.
85 %
Lex. Density
58.6 %
src
62.2%
Lex. Diversity
3.9 %
src
3.1%

* 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
142,818 Token Lines
Src Density
62.2%
Src Diversity
3.1%
Syntactic Error Report

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

868
Mismatch
139
Fixed
69
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) 2026-09-17 15:28
Re-Import (S4) 2026-09-17 13:39
Splitter (S0) 2026-09-17 12:52
Corrector (S3) 2026-09-17 11:59
Translator (S2) 2026-09-17 11:24
Tagger (S1) 2026-09-16 23:53
Tag Repair (S6) 2026-02-19 00:33
Validator (S5) 2026-02-17 00:33
Splitter (S0) 2026-02-05 17:54

Released Archive

Austronesian Showcase

Location
Image
Video