Analisis Penerimaan Mahasiswa Terhadap Sistem Informasi Akademik (SIAKAD) dengan Metode Technology Acceptance Model (TAM)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 )
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it