ANALISIS FAKTOR-FAKTOR PENERIMAAN DAN NIAT KEBERLANJUTAN PENGGUNAAN PORTAL AKADEMIK SIAKAD STMIK AKAKOM MENGGUNAKAN TAM SERTA MODEL DELONE DAN MCLEAN
Bibliographic record
Abstract
Academic Portal SIAKAD STMIK AKAKOM with address siakad.akakom.ac.id, was built with the aim of improving the quality of performance and services as well as the means of academic interaction between lecturers, students and academic departments based on information technology. Evaluate the success of its application is necessary to always meet its objectives. Based on previous research and basic theory of TAM and DeLone and McLean model hence researcher make model to analyze acceptance factors and intention of sustainability use SIAKAD STMIK AKAKOM. Analyzer used is PLS 3.0. TAM describes the behavioral factors of computer users based on the beliefs, attitudes, intention, and user behaviour relationship. The DeLone and McLean model is an information system success model with three parts of the instrument, the first used to measure the technical success of the system quality, the two measures of semantic success ie the quality of information, and the three measures of effective information system is use, user satisfaction, individual impact and organizational impact. The results of empirical research indicate variable of Information Quality and System Quality that affect the User Satisfaction towards SIAKAD STMIK AKAKOM, while the sustainability intention using SIAKAD STMIK AKAKOM determined by User Satisfaction variable.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".