Strategi Perencanaan Sistem Informasi Dan Teknologi Informasi Di Lingkungan Perguruan Tinggi
Bibliographic record
Abstract
Nowadays, many universities trying to implement Information System and Information Technology to support academic activities, but the some how there is still lacking. The problems come up when universities was mistake when initialize of what they needs. Utilization of applications of SI / IT that are not yet optimal include the lack of contribution, integration and innovation is suspected to be the cause factor. The most important thing to plan SI / IT is understanding the current condition to initialize the business needs through SWOT and Critical Success Factors analysis. The analysis will generate McFarlan portfolio matrix to determine the required applications by dividing it into 4 quadrants: support, key operational, strategic and high potential. The result of this application portfolio planning is a list of required applications now, in the future and mapping each of the proposed applications based on each function or part of the organization. From that results is expected gradually all academic activities can be automated through the presentation of accurate information and up-to-date also able to provide one stop service for academic activities than it can reduce the operational costs to support academic activities.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".