Analisis Penerimaan Mahasiswa Terhadap Sistem Informasi Akademik (SIAKAD) dengan Metode Technology Acceptance Model (TAM)
Bibliographic record
Abstract
Computer based data processing is expected to improve user performance, but the computer was not completely accepted by individuals. STMIK AKAKOM in one of their activities applied information technology to SIAKAD that provides some features for learning activities on campus The analysis of SIAKAD acceptance by students using the technology acceptance model (TAM), with partial least square , is to know how the behavior of SIAKAD users as the end user? It would be related to usefulness (PU), ease of use (PEOU), attitude toward using (ATU), and behavioral intention to use (BITU). From the analysis of data, the results is mention that there were a positive and significant influence between the variables. Technology acceptance factors that inflict attitude to use SIAKAD by student in their learning activities is the ease of use and usefulness )
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".