Penjadwalan Housekeepers Hotel Pada Era Pandemi COVID-19 dengan Pendekatan Goal Programming
Bibliographic record
Abstract
Housekeepers mempunyai peranan penting dalam oprasional hotel karena kebersihan dan kenyamanan merupakan daya tarik hotel, terutama pada era pandemi COVID-19. Namun, pada saat pandemi banyak industri perhotelan yang mengalami penurunan nilai Tingkat Penghunian Kamar (TPK) sehingga menyebabkan beberapa hotel terpaksa tutup termasuk di wilayah Jawa Barat. Hal tersebut mengakibatkan beberapa pegawai dirumahkan termasuk Housekeepers, maka dari itu penjadwalan harus diatur kembali agar tidak ada pengurangan pegawai. Penelitian ini dimaksudkan untuk menentukan penjadwalan Housekeepers dengan pendekatan Goal Programming di era pandemi COVID-19 studi kasus di Hotel R Kota Bandung. Perbedaan pada saat pandemi COVID-19 ini terdapat pada jumlah Housekeepers yang berbeda dikarenakan ada pengurangan pada saat pandemi, sehingga dapat menambah beban kerja bagi Housekeepers lain. Hal tersebut dibuktikan dari hasil penjadwalan menggunakan software LINGO 11.0 yang disesuaikan dengan fungsi tujuan untuk meminimumkan penyimpangan pada setiap kendala. Dari hasil yang didapatkan diperoleh perbedaan dari jumlah jam kerja serta libur bagi Housekeepers sebelum dan sesudah pandemi COVID-19 akan tetapi memiliki penyimpangan sama dengan nol.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".