Aplikasi Waste Assessment Model (WAM) Pada Proses Perencanaan Anggaran Menggunakan Sistem SILUNA
Bibliographic record
Abstract
Peningkatan suatu kualitas baik produk maupun proses dapat dilakukan dengan menerapkan proses audit. Salah satu proses audit yang dapat dilakukan adalah dengan mengidentifikasi adanya suatu pemborosan. Pemborosan itu sendiri merupakan suatu proses yang tidak bernilai tambah. Metode audit yang digunakan untuk mengidentifikasi terdapatnya pemborosan adalah Lean. Penentuan dan eliminasi suatu proses yang tidak memberikan suatu nilai tambah merupakan fokus utama dari Lean. Dalam penerapannya, Lean memiliki metode untuk mengidentifikasi suatu pemborosan, metode tersebut adalah Waste Assessment Model (WAM). Penerapan WAM difokuskan untuk mengidentifikasi pemborosan dalam proses perencanaan anggaran menggunakan sistem SILUNA pada Bagian Perencanaan di Biro Perencanaan dan Keuangan Universitas Udayana. Penelitian ini mendapatkan hasil berupa 2 (dua) pemborosan dengan nilai paling tinggi, yaitu : defect merupakan waste urutan pertama dengan persentase 19,79% dan waste yang berada pada urutan berikutnya adalah inventory dengan persentase 16,35%. Kata Kunci— Lean; Waste; Waste Assessment Model
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".