ENTERPRISE ARCHITECTURE SEBAGAI STRATEGI DALAM OPTIMALISASI PROSES DAN TEKNOLOGI MENGGUNAKAN TOGAF ADM
Bibliographic record
Abstract
PT XYZ merupakan perusahaan yang menawarkan audit pihak ketiga untuk menyediakan jasa sertifikasi sumberdaya alam dan lingkungan. Dalam menjalankan perusahaannya, PT XYZ memiliki layanan utama pada fungsi sertifikasi yaitu Audit Sertifikasi dan Penerbitan Dokumen Legalitas. Seiring dengan kemajuan teknologi informasi dan komunikasi, PT XYZ memiliki beberapa kendala dan membutuhkan strategi teknologi informasi yang tepat untuk menyelesaikan permasalahannya. Pertukaran data yang belum efektif merupakan salah satu kendala yang terjadi di fungsi sertifikasi yang menyebabkan munculnya keluhan pelanggan terhadap kecepatan layanan. Dalam penelitian ini akan membahas perancangan Enterprise Architecture sebagai strategi dalam optimalisasi proses dan teknologi menggunakan TOGAF ADM. Perancangan tersebut dimulai dari preliminary phase, architecture vision, business architecture, data architecture, application architecture, technology architecture, opportunities and solution, dan migration planning. Dengan blueprint dan IT Roadmap sebagai hasil akhir dari penelitian ini, diharapkan akan membantu PT XYZ dalam menyelesaikan permasalahannya segaligus menjadi strategi dalam optimalisasi proses dan teknologi yang tepat.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".