Enterprise Architecture Smart Online Education menggunakan metode TOGAF-ADM
Bibliographic record
Abstract
Perkembangan teknologi informasi saat ini sudah sangat mendukung kegiatan dalam bidang apapun. Terutama dalam bidang pendidikan, sangat penting pengaruhnya dengan adanya peranan teknologi informasi. Apalagi dalam kondisi masa pandemi Covid-19 atau setelah pandemi Covid-19 peranannya sangat mendukung. Ada beberapa semangat bahwa belajar tidak terikat dalam tempat dan waktu. Inilah sebenarnya era industri 4.0, dimana dalam kehidupan saat ini semuanya sudah menggunakan teknologi informasi. Dunia pendidikan telah banyak memanfaatkan teknologi informasi, salah satunya adalah Smart pendidikan secara Online. Karena pendidikan online ini dapat digunakan tanpa dibatasi dengan ruang dan waktu. Dimana saja siswa dapat memperoleh informasi atau pembelajaran secara mandiri dengan tutorial video yang disediakan dalam Platform pendidikan Online. Sebelum melakukan pengembangan dari Platform pendidikan Online, perlu dilakukan Information Technology Planning dimana pembuatan Enterprise Architecture merupakan Roadmap dari Platform tersebut. Enterprise Architecture dalam penelitian ini menggunakan lima tahap dari delapan tahap yang ada dalam The Open Group Architecture Framework (TOGAF). Penelitian ini bertujuan untuk menghasilkan Blueprint Platform Smart Pendidikan Online yang memanfaatkan metode TOGAF Framework. Blueprint tersebut membahas Preliminary Phase, Architecture Vision, Business Architecture, Application Architecture, Information Architecture, Technology Architecture dan Opprotunities and Solution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".