Bahasa Indonesia, Melayu, Filipino
Memuat data interpretasi naratif secara real-time...
The Full Story
Planet Zoo adalah game simulasi manajemen kebun binatang yang super detail, di mana lo dilatih sama Bernie Goodwin buat bikin habitat yang gokil dan menyelamatkan hewan langka. Gamenya asik banget, tapi bakal jauh lebih nendang kalau lo paham tiap dialog dan detail Zoopedia-nya pake bahasa kita sendiri. Nah, mod lokalisasi ini adalah 'obra maestra' gue yang nge-handle 210.919 kata lewat engine neural pipeline 8 tahap supaya bahasa Indo, Melayu, dan Filipina-nya gak kaku kayak robot. Gue bener-bener perhatiin personality tiap karakter, dari humor opa Bernie sampe gaya bicara korporat si Dominic Myers, semua gue bumbuin pake slang dan budaya lokal yang pas. Bukan cuma translate asal-asalan, ini project yang gue kerjain pake hati biar lo berasa punya kebun binatang sendiri di komplek rumah. Wajib download kalo mau mainnya maksimal!
Experimental Build
Author's Notes
=== Audit Teknis & Semantik Lokalisasi PLANET ZOO ===
1. SKALA LINGUISTIK & CAKUPAN
- Skala Proyek: Sekitar 210,919 kata diproses melalui alur neural 8-tahap.
- Cakupan Bahasa: Dukungan trilingual penuh untuk pasar Indonesia, Malaysia, dan Filipina.
- Status Kelengkapan: Indonesia: 94.5%, Malay: 95.2%, Filipino: 90.1%
- Analisis Variasi Leksikal: Source -> Density: 68.4% | Diversity: 5.7%, Indonesia -> Density: 78.3% | Diversity: 7.0%, Malay -> Density: 75.3% | Diversity: 5.9%, Filipino -> Density: 59.8% | Diversity: 6.3%
2. VALIDASI NEURAL & AKURASI
- Skor Keselarasan Semantik (Platt Score): Indonesia: 87%, Malay: 85%, 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 7 karakter unik.
- Pemulihan Struktur Otomatis (Tag Repair): 230 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 2.0% watermarks. Support this project on Trakteer or Ko-fi to download NON-WATERMARKED version.
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