PERBANDINGAN KUALITAS WEBSITE KEMENTERIAN KOMUNIKASI DAN INFORMATIKA MENGGUNAKAN METODE WEBQUAL 4.0 (STUDI KASUS WEBSITE KOMINFO KAB. KARO DAN KOTA LHOKSEUMAWE)
Bibliographic record
Abstract
WebQual salah satu metode pengukuran kualitas website yang populer, terutama dalam layanan publik. Penelitian ini melibatkan tiga kelompok responden yang berbeda: mahasiswa IT, masyarakat awam non-IT, dan profesional di bidang IT terhadap website (Kominfo) di Kabupaten Karo dan Kota Lhokseumawe. Masyarakat awam/non-IT di Kabupaten Karo memberikan penilaian yang cukup baik dengan nilai rata-rata Expectancy Confirmation (EC) sebesar 3,42, sementara masyarakat awam/non-IT di Kota Lhokseumawe memberikan penilaian yang sedikit lebih rendah dengan nilai rata-rata EC sebesar 2,74. Mahasiswa IT dari kedua wilayah memberikan penilaian yang cukup baik dengan nilai rata-rata EC masing-masing sebesar 3,71 (Kabupaten Karo) dan 3,70 (Kota Lhokseumawe). Namun, profesional web developer di kedua wilayah memberikan penilaian yang rendah dengan nilai rata-rata EC sebesar 1,85 (Kabupaten Karo) dan 2,12 (Kota Lhokseumawe).Secara keseluruhan, evaluasi menunjukkan bahwa rata-rata nilai Expectancy Confirmation (EC) adalah 2,74, dengan ketidaksesuaian antara harapan dan kinerja website yang masih dapat diterima dengan rata-rata nilai Disconfirmation (D) sebesar 0,24. Penilaian keseluruhan terhadap kualitas website (Overall Website Quality, OWQ) rendah dengan rata-rata nilai sebesar 1,52. Profesional web developer memberikan bobot penting yang tinggi pada beberapa pertanyaan dalam evaluasi (Importance Weight, IW) dengan rata-rata nilai sebesar 6,71.
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How this classification was reachedexpand
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".