Aplikasi Pelacakan Alumni STMIK AKAKOM Berbasis Sistem Informasi Geografis
Bibliographic record
Abstract
Alumni is a product of an educational institution. The quality of the alumni shows the quality of the educational institution. The fact is increasingly felt, especially for college alumni. This is because alumni of college will directly come into contact with the world of work. Tracer study activity is one of the activities that have a very strategic value in the development of a college. STMIK Akakom is one of the universities in the city of Yogyakarta is required to always mempebaiki quality of education process accompanied by efforts to increase its relevance in the framework of global competition. In addition Tracer study is one effort that is expected to provide information to evaluate educational outcomes in STMIK Akakom. This information is used for further development in ensuring educational quality. This research produces alumni application of STMIK Akakom alumni by utilizing Geographic information system to map the location where alumni work, besides that it also displays alumni data in the form of year of admission, graduation year, long waiting time to work first after graduation
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".