Analisa Kualitas Website Sekolah Menengah Kejuruan Negeri SMKN Di Kota Makassar Dengan Menggunakan Webqual Modifikasi
Bibliographic record
Abstract
Keberadaan website sekolah sangat penting baik bagi siswa, sekolah maupun masyarakat. Namun keberadaan website sekolah tersebut belum pernah dianalisa untuk mengetahui sejauh mana kualitas layanan yang sudah diberikan kepada pengguna. Sehingga penelitian ini dilakukan untuk mengetahui kualitas layanan website Sekolah Menengah Kejuruan Negeri di kota Makassar. Metode yang digunakan dalam penelitian ini adalah deduktif kuantitatif dengan menggunakan Webqual versi 4.0 yang telah dimodifikasi dengan menambahkan dimensi kualitas antarmuka pengguna. Hasil penelitian menunjukkan bahwa terdapat gap yang cukup besar antara realitas web SMK dengan harapan pengguna akhir pada masing-masing dimensi. Gap paling besar terjadi pada dimensi kualitas layanan interaksi yaitu -0.98. Dan gap paling rendah terjadi pada dimensi kualitas antarmuka pengguna yaitu -0.82.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 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".