ARCHITECTURAL DESIGN INFORMATION SYSTEM HEALTH CRISIS MANAGEMENT USING FRAMEWORK ZACHMAN
Bibliographic record
Abstract
Bencana merupakan suatu hal yang sangat mengganggu aktivitas manusia, wilayah kejadian bencana dapat mengganggu kelancaran aktivitas ekonomi, menghancurkan sendi-sendi sosial, dan membahayakan keberlangsungan hidup komunitas. Semua kejadian bencana menimbulkan krisis kesehatan antara lain lumpuhnya pelayanan kesehatan, korban mati, korban luka, pengungsi, masalah gizi, masalah ketersediaan air bersih, masalah sanitasi lingkungan, penyakit menular dan stres/gangguan kejiwaan.
 
 Penanganan tanggap darurat krisis kesehatan akibat bencana membutuhkan strategi pemanfaatan dan peningkatan dukungan sistem informasi, diterapkan untuk keselarasan dalam perencanaan, pelaksanaan dengan strategi bisnis enterprise. Secara garis besar sistem informasi ini dirancang untuk membantu penanganan krisis kesehatan akibat bencana alam.
 
 Metode yang digunakan dalam penelitian ini adalah Enterprise Architecture Planning berdasarkan Zachman framework yang mencakup kolom What, How, Where dan baris Scope, Enterprise Model, System Model, sedangkan perangkat lunak digunakan dalam membantu penggambaran sistem yaitu dengan Unified Modelling Language (UML). Perancangan arsitektur sistem informasi penelitian ini menghasilkan 49 entitas data, 32 kandidat aplikasi dan arsitektur teknologi.
 
 Roadmap memberikan gambaran prioritas aplikasi yang akan dibuat dalam perancangan arsitektur sistem informasi pengelolaan krisis kesehatan pada dinas kesehatan provinsi Kalimantan Selatan yang akan menunjang proses bisnisnya sehari-hari.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".