ANALISIS OPTIMASI WAKTU DAN BIAYA DENGAN METODE TIME COST TRADE OFF
Bibliographic record
Abstract
Lokasi penelitian terletak di di Dukuh Balong Kelurahan Timbulharjo Kecamatan Sewon Kabupaten Bantul Daerah Istimewa Yogyakarta. Metode yang digunakan adalah Metode Time Cost Trade Off melakukan penambahan tenaga kerja, dan, jam kerja lembur. Pengolahan data menggunakan data sekunder seperti rancangan anggaran biaya, time schedule rencana dan realisasi, laporan mingguan, dan gambar kerja. Hasil analisis penelitian dapat dilihat bahwa terdapat perbedaan antara biaya dan durasi akibat penambahan jam kerja (lembur) dan penambahan tenaga kerja. Pada penambahan lembur 1 jam dengan biaya total Rp 289.876.705,23, selanjutnya pada penambahan lembur 2 jam dengan biaya total Rp 308.391.171,11 dan, pada penambahan lembur 3 jam dengan biaya total Rp 327.196.816,63 dengan total durasi setelah di lakukan lembur menjadi 61 hari jika dibandingkan dengan penambahan tenaga kerja dengan biaya total Rp 156.919.168,62 dengan total durasi setelah di lakukan penambahan tenaga kerja menjadi 59 hari.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".