Leveraging Enterprise Architecture to Empower KOMINFO's Business Core Operations: A PMO Perspective
Bibliographic record
Abstract
The Sky Bridge (Tol Langit) Program is an Indonesian government’s strategic project aimed at digital transformation in the 3T regions (Tertinggal, Terdepan, Terluar - Underdeveloped, Frontline, Outermost). It requires thorough planning and integrated management for its implementation. A specialized unit with a helicopter view perspective is needed to ensure and oversee the alignment of processes. This important role is managed by the Project Management Office (PMO). One of the challenges PMO faces in ensuring an end-to-end process alignment is identifying the appropriate digital resources to support the process. This is where the Enterprise Architecture (EA) framework plays a crucial role as a blueprint for the organization's digital landscape. This reference helps map out existing data, applications, and business processes. Having this blueprint allows PMO to have a holistic view and make targeted decisions. EA also helps identify existing applications that can be integrated with new programs, avoiding unnecessary duplication. The use of ArchiMate, a language for enterprise architecture modeling, assists PMOs in planning digital transformations considering all aspects - business needs, applications, and technology. In short, a well-defined EA framework empowers PMOs to navigate the complexities of digital transformation in the telecommunications sector to ensure the successful implementation of the Sky Bridge Program.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".