Bahasa Indonesia, Melayu, Filipino
Hogwarts Legacy is an immersive, open-world action RPG. Now you can take control of the action and be at the center of your own adventure in the wizarding world.
The Full Story
Mainkan RPG penyihir tahun 1890-an yang super megah, di mana kamu bakal nguasain Sihir Kuno buat bantai para poacher dan goblin yang rese. Temenan bareng Sebastian Sallow yang penuh masalah atau Poppy Sweeting yang pecinta hewan sambil keliling Hogwarts yang gede banget. Mod ini wajib punya karena kita nggak pake terjemahan kaleng-kaleng; ada 421.474 kata yang diproses pake AI neural 8-tahap biar bahasanya nendang dan dapet banget slang Indonesianya. Tiap karakter punya gaya ngomong sendiri-sendiri, jadi nggak bakal kaku kayak baca buku sejarah sekolah. Ini proyek ambis penuh passion biar pengalaman kalian jadi murid Hogwarts makin berasa lokal dan seru abis!
Experimental Build
Author's Notes
=== Audit Teknis & Semantik Lokalisasi HOGWARTS LEGACY ===
1. SKALA LINGUISTIK & CAKUPAN
- Skala Proyek: Sekitar 421,474 kata diproses melalui alur neural 8-tahap.
- Cakupan Bahasa: Dukungan trilingual penuh untuk pasar Indonesia, Malaysia, dan Filipina.
- Status Kelengkapan: Indonesia: 98.3%, Malay: 98.5%, Filipino: 97.2%
- Analisis Variasi Leksikal: Source -> Density: 61.8% | Diversity: 3.4%, Indonesia -> Density: 70.1% | Diversity: 4.5%, Malay -> Density: 69.9% | Diversity: 3.5%, Filipino -> Density: 59.1% | Diversity: 4.3%
2. VALIDASI NEURAL & AKURASI
- Skor Keselarasan Semantik (Platt Score): Indonesia: 87%, Malay: 85%, Filipino: 85%
(Skor ini mengukur seberapa akurat terjemahan mempertahankan makna asli dari teks sumber.)
- Gaya Bahasa Karakter: Penyesuaian gaya (gaul, formal, santai) telah diterapkan pada 34 karakter unik.
- Pemulihan Struktur Otomatis (Tag Repair): 139 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.
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Linguistic Analysis Report
Discourse analysis using Gemma embeddings. Classifies rhetorical register across the corpus to ensure tonal consistency with source narrative assets.
Emotional tone mapped via dot-product similarity between extracted dialog embeddings and predefined sentiment anchors using zero-shot semantic alignment.
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.
* Sim = Cosine Similarity (Vector Space) · Density = Content/Total Tokens · Diversity = TTR (Type-Token Ratio) · "src" = Source Baseline · Named Entities enforced via GLiNER mining.
Heuristic markup verification utilizing multi-pass validation and correction to ensure syntactical integrity of control codes and visual tags.
Name
Narrative Profile
Associated Entities
Semantic Archetypes
NLP Pipeline Intelligence