Pengembangan Sistem Basis Data dalam Pembuatan Aplikasi Monitoring Call Center
Bibliographic record
Abstract
Tujuan penelitian ini adalah untuk membuat aplikasi monitoring call center dalam mencegah terjadinya tindakan diskriminatif, memudahkan laporan kepada atasan, dan memonitoring konsistensi terhadap customer service dengan dengan menganalisis dan merancang sistem basis data didalam aplikasi tersebut. Masalah yang dihadapi oleh Badan Penanggulangan Bencana Daerah Provinsi DKI Jakarta adanya kesulitan dalam memantau efisiensi agen call center, memantau keterlibatan agen customer service dalam melayani panggilan pekerjaan, dan pemeriksaan cepat secara real time. Metode yang digunakan dalam penelitian ini menggunakan Fact Finding yang dilakukan dengan studi langsung ke lapangan pihak terkait, wawancara, dan mempelajari dokumentasi perusahaan yang ditindaklanjuti dengan studi kepustakaan.. Hasil dari pembuatan aplikasi monitoring call center ini memudahkan pengawas dalam memonitoring setiap agen call center tanpa mengganggu percakapan dengan pelanggan dan meningkatkan produktivitas pada tingkat yang lebih tinggi dan setiap agen call center dapat menjaga hubungan dengan pelanggan dengan tujuan kualitas pelayanan terhadap pelanggan dengan pihak terkait.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".